This document summarizes several common grading systems used in neurosurgical practice. It groups the grading systems by subspecialty area, including cranial, vascular, and spinal neurosurgery. For each grading system, a brief description is provided along with a table outlining the criteria. Some of the grading systems discussed include the Glasgow Coma Scale for assessing consciousness, the Spetzler-Martin grade for evaluating arteriovenous malformation resection risk, and the Hunt and Hess classification for subarachnoid hemorrhage severity. The goal of the summary is to provide an easy reference for clinicians on commonly used neurosurgical grading scales.
The Glasgow Coma Scale (GCS) objectively measures a patient's level of consciousness and awareness following acute brain injury or illness. It assesses eye opening, verbal response, and motor response on a scale of 3-15, with lower scores indicating more severe impairment. The GCS allows for standardized communication of a patient's condition and guides management decisions. It has been shown to correlate with patient outcomes, with mortality increasing as GCS scores decrease. The GCS is a core assessment for many clinical guidelines involving trauma, critical illness, and neurological conditions.
A hybrid model for heart disease prediction using recurrent neural network an...BASMAJUMAASALEHALMOH
This document presents research on developing a hybrid deep learning model using recurrent neural networks (RNN) and long short-term memory (LSTM) to predict heart disease. The researchers created a model that classifies synthetic cardiac data using different RNN and LSTM approaches with cross-validation. They evaluated the system's performance using various machine learning methods and found that the deep hybrid learning approach was more accurate than either classic deep learning or machine learning alone. The document provides background on heart disease and motivation for developing a more accurate predictive model, describes the methodology used including the dataset, and outlines the experimental setup and algorithm.
CARDIOVASCULAR DISEASE DETECTION USING MACHINE LEARNING AND RISK CLASSIFICATI...indexPub
The global prevalence of heart disease indicates a major public health issue. It causes shortness of breath, weakness, and swollen ankles. Early heart disease diagnosis is difficult with current approaches. Hence, a better heart disease detection tool is needed. Treatment requires more than just diagnosis. Risk classification is critical for accurate diagnosis and treatment. In this analysis, a novel cardiovascular disease (CVD) detection paradigm using machine learning (ML) and risk classification based on a weighted fuzzy system is proposed. The system is developed based on ML algorithms such as artificial neural network (ANN) and Long Short-Term Memory (LSTM) and uses standard feature selection techniques knowns as Principal Component Analysis (PCA). Furthermore, the cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The accuracy-based performance measuring metrics are used for the assessment of the performances of the classifiers. Finally, the outcomes revealed that the proposed model achieved an accuracy of 94.01% which is higher than another conventional model developed in this domain. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.
IRJET- A System to Detect Heart Failure using Deep Learning TechniquesIRJET Journal
This document proposes a system to detect heart failure using deep learning techniques. The system uses a boosted decision tree to initially detect the probability that a patient is prone to heart failure. If the probability is over 50%, the patient's ECG recordings are passed to a convolutional neural network (CNN) for more accurate detection of heart failure. The CNN is trained on a dataset of 60,000 ECG recordings. The system also aims to detect the subtype of heart failure using an SVM algorithm trained on data distinguishing systolic vs diastolic heart failure. The overall goal is to accurately detect heart failure at early stages to improve outcomes.
A Comparative Analysis of Heart Disease Prediction System Using Machine Learn...IRJET Journal
This document presents a literature review and comparative analysis of existing machine learning techniques used for heart disease prediction. It discusses several studies that have applied techniques like decision trees, naive Bayes, KNN, random forest, SVM and neural networks to heart disease datasets. The highest prediction accuracies reported range from 85-100%, with random forest and KNN performing best in some studies. The document aims to help researchers develop better heart disease prediction systems by understanding existing methodologies and identifying areas for improvement.
This summarizes a research paper that applies machine learning classifiers to an ECG dataset to predict heart disease. It tested five machine learning algorithms (Support Vector Machine, Logistic Regression, K-nearest Neighbors, Naive Bayes, Ensemble Voting Classifier) on a dataset of 1190 records combining five ECG datasets. The Support Vector Machine model achieved the highest accuracy of 85.49% at predicting heart disease. The study aims to enable early diagnosis of heart disease and increase survival rates.
Heart Disease Prediction Using Machine Learning TechniquesIRJET Journal
This document describes a study that used five machine learning algorithms to predict heart disease: Random Forest classification, Support Vector Machine, AdaBoost Classifier, Logistic Regression, and Decision Tree Classifier. The algorithms were tested on a dataset of 270 patients described by 14 attributes. Random Forest classification achieved the highest test accuracy of 85.22%, compared to accuracies ranging from 67.43% to 81.23% for the other algorithms. Therefore, the study concludes that Random Forest classification is the best performing algorithm for predicting heart disease based on this dataset and analysis.
Identifying Significant Antipsychotic-Related Side Effects in Patients on a Community Psychiatric Rehabilitation Unit-A Feasibility Study of The Glasgow Antipsychotic Side-Effect Scale (GASS) by Ahmed Saeed Yahya* in crimson publishers: Journal of Physical Medicine and Rehabilitation
Antipsychotic side-effects are common and are an important determinant of non-adherence and consequent relapse. Most rating scales for the identification of these are lengthy and complicated. This report reviews the medical literature on the Glasgow antipsychotic side-effect scale (GASS)-a brief and validated rating scale to measure the unwanted effects of antipsychotics. We administered the GASS to fourteen in-patients in a United Kingdom-based Community Psychiatric Rehabilitation Unit. The objective was to establish the utility of the GASS in this setting and to make recommendations on how this tool could be used in clinical practice to improve adherence to antipsychotic medication.
https://crimsonpublishers.com/epmr/fulltext/EPMR.000529.php
For more Open access journals in Crimson Publishers
Please click on: https://crimsonpublishers.com/
For more articles in Examines in Physical Medicine & Rehabilitation
Please click on: https://crimsonpublishers.com/epmr/
The Glasgow Coma Scale (GCS) objectively measures a patient's level of consciousness and awareness following acute brain injury or illness. It assesses eye opening, verbal response, and motor response on a scale of 3-15, with lower scores indicating more severe impairment. The GCS allows for standardized communication of a patient's condition and guides management decisions. It has been shown to correlate with patient outcomes, with mortality increasing as GCS scores decrease. The GCS is a core assessment for many clinical guidelines involving trauma, critical illness, and neurological conditions.
A hybrid model for heart disease prediction using recurrent neural network an...BASMAJUMAASALEHALMOH
This document presents research on developing a hybrid deep learning model using recurrent neural networks (RNN) and long short-term memory (LSTM) to predict heart disease. The researchers created a model that classifies synthetic cardiac data using different RNN and LSTM approaches with cross-validation. They evaluated the system's performance using various machine learning methods and found that the deep hybrid learning approach was more accurate than either classic deep learning or machine learning alone. The document provides background on heart disease and motivation for developing a more accurate predictive model, describes the methodology used including the dataset, and outlines the experimental setup and algorithm.
CARDIOVASCULAR DISEASE DETECTION USING MACHINE LEARNING AND RISK CLASSIFICATI...indexPub
The global prevalence of heart disease indicates a major public health issue. It causes shortness of breath, weakness, and swollen ankles. Early heart disease diagnosis is difficult with current approaches. Hence, a better heart disease detection tool is needed. Treatment requires more than just diagnosis. Risk classification is critical for accurate diagnosis and treatment. In this analysis, a novel cardiovascular disease (CVD) detection paradigm using machine learning (ML) and risk classification based on a weighted fuzzy system is proposed. The system is developed based on ML algorithms such as artificial neural network (ANN) and Long Short-Term Memory (LSTM) and uses standard feature selection techniques knowns as Principal Component Analysis (PCA). Furthermore, the cross-validation method has been used for learning the best practices of model assessment and for hyperparameter tuning. The accuracy-based performance measuring metrics are used for the assessment of the performances of the classifiers. Finally, the outcomes revealed that the proposed model achieved an accuracy of 94.01% which is higher than another conventional model developed in this domain. Additionally, the proposed system can easily be implemented in healthcare for the identification of heart disease.
IRJET- A System to Detect Heart Failure using Deep Learning TechniquesIRJET Journal
This document proposes a system to detect heart failure using deep learning techniques. The system uses a boosted decision tree to initially detect the probability that a patient is prone to heart failure. If the probability is over 50%, the patient's ECG recordings are passed to a convolutional neural network (CNN) for more accurate detection of heart failure. The CNN is trained on a dataset of 60,000 ECG recordings. The system also aims to detect the subtype of heart failure using an SVM algorithm trained on data distinguishing systolic vs diastolic heart failure. The overall goal is to accurately detect heart failure at early stages to improve outcomes.
A Comparative Analysis of Heart Disease Prediction System Using Machine Learn...IRJET Journal
This document presents a literature review and comparative analysis of existing machine learning techniques used for heart disease prediction. It discusses several studies that have applied techniques like decision trees, naive Bayes, KNN, random forest, SVM and neural networks to heart disease datasets. The highest prediction accuracies reported range from 85-100%, with random forest and KNN performing best in some studies. The document aims to help researchers develop better heart disease prediction systems by understanding existing methodologies and identifying areas for improvement.
This summarizes a research paper that applies machine learning classifiers to an ECG dataset to predict heart disease. It tested five machine learning algorithms (Support Vector Machine, Logistic Regression, K-nearest Neighbors, Naive Bayes, Ensemble Voting Classifier) on a dataset of 1190 records combining five ECG datasets. The Support Vector Machine model achieved the highest accuracy of 85.49% at predicting heart disease. The study aims to enable early diagnosis of heart disease and increase survival rates.
Heart Disease Prediction Using Machine Learning TechniquesIRJET Journal
This document describes a study that used five machine learning algorithms to predict heart disease: Random Forest classification, Support Vector Machine, AdaBoost Classifier, Logistic Regression, and Decision Tree Classifier. The algorithms were tested on a dataset of 270 patients described by 14 attributes. Random Forest classification achieved the highest test accuracy of 85.22%, compared to accuracies ranging from 67.43% to 81.23% for the other algorithms. Therefore, the study concludes that Random Forest classification is the best performing algorithm for predicting heart disease based on this dataset and analysis.
Identifying Significant Antipsychotic-Related Side Effects in Patients on a Community Psychiatric Rehabilitation Unit-A Feasibility Study of The Glasgow Antipsychotic Side-Effect Scale (GASS) by Ahmed Saeed Yahya* in crimson publishers: Journal of Physical Medicine and Rehabilitation
Antipsychotic side-effects are common and are an important determinant of non-adherence and consequent relapse. Most rating scales for the identification of these are lengthy and complicated. This report reviews the medical literature on the Glasgow antipsychotic side-effect scale (GASS)-a brief and validated rating scale to measure the unwanted effects of antipsychotics. We administered the GASS to fourteen in-patients in a United Kingdom-based Community Psychiatric Rehabilitation Unit. The objective was to establish the utility of the GASS in this setting and to make recommendations on how this tool could be used in clinical practice to improve adherence to antipsychotic medication.
https://crimsonpublishers.com/epmr/fulltext/EPMR.000529.php
For more Open access journals in Crimson Publishers
Please click on: https://crimsonpublishers.com/
For more articles in Examines in Physical Medicine & Rehabilitation
Please click on: https://crimsonpublishers.com/epmr/
Macquarie Neurosurgery Journal Club 2022 PPTMQ_Library
1) This randomized controlled non-inferiority trial compared full endoscopic discectomy (PTED) to open microdiscectomy (MLD) for sciatica.
2) PTED was found to be non-inferior to open MLD for reducing leg pain, with similar or better outcomes for secondary measures like function and quality of life.
3) PTED had fewer complications than open MLD and allowed for shorter hospital stays.
Genetically Optimized Neural Network for Heart Disease ClassificationIRJET Journal
This document describes a study that uses a genetically optimized neural network to classify heart disease based on patient risk factors. The study collects data on 12 risk factors from 50 patients and encodes the values for use as input to a neural network. The neural network is initially trained using backpropagation, then genetic algorithms are used to optimize the network weights and biases to improve accuracy. Confusion matrices are plotted to evaluate the accuracy of the optimized neural network at classifying patients as having heart disease or not. The approach achieves a classification accuracy of 90% on the test data.
This meta-analysis reviewed 16 randomized controlled trials comparing the effectiveness of motor control exercises (MCE) to other treatments for chronic or recurrent low back pain. The analysis found that MCE was superior to general exercise in reducing both disability in the short, intermediate, and long term, and pain in the short and intermediate term. MCE was also superior to minimal interventions like advice or placebo for both pain and disability outcomes at all time periods. Compared to spinal manual therapy, MCE demonstrated superior results for reducing disability but not pain. The studies varied in quality but provided evidence that MCE can better improve pain and disability for low back pain over the short to long term compared to other common treatments.
A COMPREHENSIVE SYSTEMATIC REVIEW FOR CARDIOVASCULAR DISEASE USING MACHINE LE...gerogepatton
The global upswing in cardiovascular disease (CVD) cases presents a critical challenge. While the
ultimate goal remains elusive, improving CVD prediction accuracy is vital. Machine learning and deep
learning are crucial for decoding complex health data, enhancing cardiac imaging, and predicting disease
outcomes in clinical practice. This systematic literature review meticulously analyses CVD using machine
learning techniques, with a particular emphasis on algorithms for classification and prediction. The metaanalysis covers 343 references from 2020 to November 2023, preceding a thorough examination of 65
selected references. Acknowledging current hurdles in CVD classification methods that impede practical
use, this systematic literature review (SLR) is conducted.
The study provides valuable insights for researchers and healthcare professionals, facilitating the
integration of clinical applications in machine learning settings related to CVD. It also aids in promptly
identifying potential threats and implementing precautionary measures. The study also recognizes
prevalent classical machine learning methods, emphasizing their clinically relevant diagnostic outcomes.
Deliberating on current trends, algorithms, and potential areas for future research offers a comprehensive
insight into the present state of affairs
A COMPREHENSIVE SYSTEMATIC REVIEW FOR CARDIOVASCULAR DISEASE USING MACHINE LE...gerogepatton
The global upswing in cardiovascular disease (CVD) cases presents a critical challenge. While the
ultimate goal remains elusive, improving CVD prediction accuracy is vital. Machine learning and deep
learning are crucial for decoding complex health data, enhancing cardiac imaging, and predicting disease
outcomes in clinical practice. This systematic literature review meticulously analyses CVD using machine
learning techniques, with a particular emphasis on algorithms for classification and prediction. The metaanalysis covers 343 references from 2020 to November 2023, preceding a thorough examination of 65
selected references. Acknowledging current hurdles in CVD classification methods that impede practical
use, this systematic literature review (SLR) is conducted.
The study provides valuable insights for researchers and healthcare professionals, facilitating the
integration of clinical applications in machine learning settings related to CVD. It also aids in promptly
identifying potential threats and implementing precautionary measures. The study also recognizes
prevalent classical machine learning methods, emphasizing their clinically relevant diagnostic outcomes.
Deliberating on current trends, algorithms, and potential areas for future research offers a comprehensive
insight into the present state of affairs.
IMBALANCED DATASET EFFECT ON CNN-BASED CLASSIFIER PERFORMANCE FOR FACE RECOGN...ijaia
Facial Recognition is integral to numerous modern applications, such as security systems, social media
platforms, and augmented reality apps. The success of these systems heavily depends on the performance
of the Face Recognition models they use, specifically Convolutional Neural Networks (CNNs). However,
many real-world classification tasks encounter imbalanced datasets, with some classes significantly
underrepresented. Face Recognition models that do not address this class imbalance tend to exhibit poor
performance, especially in tasks involving a wide range of faces to identify (multi-class problems). This
research examines how class imbalance in datasets impacts the creation of neural network classifiers for
Facial Recognition. Initially, we crafted a Convolutional Neural Network model for facial recognition,
integrating hybrid resampling methods (oversampling and under-sampling) to address dataset imbalances.
In addition, augmentation techniques were implemented to enhance generalization capabilities and overall
performance. Through comprehensive experimentation, we assess the influence of imbalanced datasets on
the performance of the CNN-based classifier. Using Pins face data, we conducted an empirical study,
evaluating conclusions based on accuracy, precision, recall, and F1-score measurements. A comparative
analysis demonstrates that the performance of the proposed Convolutional Neural Network classifier
diminishes in the presence of dataset class imbalances. Conversely, the proposed system, utilizing data
resampling techniques, notably enhances classification performance for imbalanced datasets. This study
underscores the efficacy of data resampling approaches in augmenting the performance of Face
Recognition models, presenting prospects for more dependable and efficient future systems.
A COMPREHENSIVE SYSTEMATIC REVIEW FOR CARDIOVASCULAR DISEASE USING MACHINE LE...ijaia
The global upswing in cardiovascular disease (CVD) cases presents a critical challenge. While the
ultimate goal remains elusive, improving CVD prediction accuracy is vital. Machine learning and deep
learning are crucial for decoding complex health data, enhancing cardiac imaging, and predicting disease
outcomes in clinical practice. This systematic literature review meticulously analyses CVD using machine
learning techniques, with a particular emphasis on algorithms for classification and prediction. The metaanalysis covers 343 references from 2020 to November 2023, preceding a thorough examination of 65
selected references. Acknowledging current hurdles in CVD classification methods that impede practical
use, this systematic literature review (SLR) is conducted.
The study provides valuable insights for researchers and healthcare professionals, facilitating the
integration of clinical applications in machine learning settings related to CVD. It also aids in promptly
identifying potential threats and implementing precautionary measures. The study also recognizes
prevalent classical machine learning methods, emphasizing their clinically relevant diagnostic outcomes.
Deliberating on current trends, algorithms, and potential areas for future research offers a comprehensive
insight into the present state of affairs.
Brough et al perspectives on the effects and mechanisms of CST a qualitative ...Nicola Brough
This document summarizes a qualitative study on the effects and mechanisms of craniosacral therapy according to users' views. 29 participants were interviewed about their experiences with craniosacral therapy. Most participants reported improvements in at least two dimensions of holistic wellbeing: body, mind and spirit. Experiences during therapy included altered perceptual states and specific sensations and emotions. Participants emphasized the importance of the therapeutic relationship. The emerging theory from the study suggests that the trusting relationship in craniosacral therapy allows clients to experience altered states of awareness, which facilitates a new understanding of the interrelatedness of body, mind and spirit and an enhanced ability to care for oneself and manage health problems.
Schemes for medical decision making a primer for traineesImad Hassan
An article on a practical road map for teaching trainees easy schemes for Clinical Decision Making. Due to appear soon on the journal "Perspectives on Medical Education"
Useful for Trainers and Trainees alike to complement and expand the PowerPoint presentation on the same subject.
PERFORMANCE ANALYSIS OF MULTICLASS SUPPORT VECTOR MACHINE CLASSIFICATION FOR ...ijcsa
Automatic diagnosis of coronary heart disease helps the doctor to support in decision making a diagnosis.
Coronary heart disease have some types or levels. Referring to the UCI Repository dataset, it divided into 4
types or levels that are labeled numbers 1-4 (low, medium, high and serious). The diagnosis models can be
analyzed with multi class classification approach. One of multi class classification approach used, one of
which is a support vector machine (SVM). The SVM use due to strong performance of SVM in binary classification. This research study multiclass performance classification support vector machine to diagnose the type or level of coronary heart disease. Coronary heart disease patient data taken from the
UCI Repository. Stages in this study is preprocessing, which consist of, to normalizing the data, divide the data into data training and testing. The next stage of multiclass classification and performance analysis.This study uses multiclass SVM algorithm, namely: Binary Tree Support Vector Machine (BTSVM), OneAgainst-One (OAO), One-Against-All (OAA), Decision Direct Acyclic Graph (DDAG) and Exhaustive
Output Error Correction Code ( ECOC). Performance parameter used is recall, precision, F-measure and
Overall accuracy. The experiment results showed that the multiclass SVM classification algorithm with the
algorithm BT-SVM, OAA-SVM and the ECOC-SVM,gave the highest Recall in the diagnosis of type or
healthy level with a value above 90%, precision 82.143% and 86.793% F-measure,. For all kinds of
algorithms, except binary OAA-SVM algorithm gave the highest recall 0.0% for the type or level of sickhigh
and sick-serious, and ECOC- SVM algorithm gave the highest recall 0.0% for sick-medium and sickserious.
While the type or level other, the performance of recall, precision and F-measure between 20% -
30%,. The conclusion that can be drawn is that the approach to the multiclass classification algorithm BTSVM,
OAO-SVM, DDAG-SVM to diagnosis the type or level of coronary heart disease provides better performance, than the binary classification approach.
Economic And Humanistic Outcomes Of Post Acs In Cardiac Rehabilitation Progra...guestaf1e4
The study evaluated a modified cardiac rehabilitation program (MCRP) compared to a conventional program in patients after acute coronary syndrome. Quality of life was measured using SF-36 at baseline and 12 months. MCRP showed improved physical and mental health scores compared to baseline and controls. MCRP was found to be highly cost-effective with minimal incremental cost ratios compared to usual care without rehabilitation. The study demonstrated that MCRP can improve patient outcomes and is a low-cost intervention that should be implemented more widely.
An Evaluation of A Stroke Rehabilitation Study At Rotman Research InstituteJoanna (Yijing) Rong
This document provides an overview of the co-op placement of Joanna Rong at the Rotman Research Institute. The placement involved assisting with a stroke rehabilitation study comparing Music-Supported Rehabilitation (MSR) to Graded Repetitive Arm Supplementary Program (GRASP). Three assessment methods were used: magnetoencephalography (MEG), behavioral tests, and MRI scans. The document discusses the purpose and limitations of the assessments.
The Analysis of Performace Model Tiered Artificial Neural Network for Assessm...IJECEIAES
The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, that is the tiered approach. This makes the assessment process should conduct a thorough examination. This study aims to analyze the performance of a tiered model assessment. The assessment system is divided into several levels, with reference to the stages of the inspection procedure.The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The effect indicates that the level above gives performance improvement and or strengthens the performance at the previous level.
Discovering Abnormal Patches and Transformations of Diabetics Retinopathy in ...cscpconf
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.
Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to
screen abnormalities through retinal fundus images by clinicians. However, limited number of
well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a
big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy
severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images
in the training set, we also automatically segment the abnormal patches with an occlusion test,
and model the transformations and deterioration process of DR. Our results can be widely used
for fast diagnosis of DR, medical education and public-level healthcare propagation.
DISCOVERING ABNORMAL PATCHES AND TRANSFORMATIONS OF DIABETICS RETINOPATHY IN ...csandit
Diabetic retinopathy (DR) is one of the retinal diseases due to long-term effect of diabetes.Early detection for diabetic retinopathy is crucial since timely treatment can prevent
progressive loss of vision. The most common diagnosis technique of diabetic retinopathy is to screen abnormalities through retinal fundus images by clinicians. However, limited number of well-trained clinicians increase the possibilities of misdiagnosing. In this work, we propose a big-data-driven automatic computer-aided diagnosing (CAD) system for diabetic retinopathy severity regression based on transfer learning, which starts from a deep convolutional neural
network pre-trained on generic images, and adapts it to large-scale DR datasets. From images in the training set, we also automatically segment the abnormal patches with an occlusion test,and model the transformations and deterioration process of DR. Our results can be widely used for fast diagnosis of DR, medical education and public-level healthcare propagation.
An optimal heart disease prediction using chaos game optimization‑based recur...BASMAJUMAASALEHALMOH
The document proposes a Chaos Game Optimization based Recurrent Neural Network (CGO-RNN) model for predicting heart disease. It uses Kernel Principal Component Analysis (KPCA) for dimensionality reduction and feature extraction. The CGO algorithm tunes the hyperparameters of the RNN model to improve accuracy. The model is evaluated on heart disease datasets and achieves prediction accuracies of 98.99-98.54%, demonstrating improved performance over other methods.
Jurnal internasional ijair erwin panggabean tahun 2018erwin panggabean
This document compares the Dempster-Shafer method and certainty factor method for diagnosing stroke diseases using an expert system. It summarizes a study conducted at Bhayangkara Hospital in Medan, Indonesia, where patient data and input from a neurologist were used to test the two methods. The results found that the certainty factor method produced more accurate diagnoses than the Dempster-Shafer method according to the symptom knowledge base and hospital cases. Specifically, the certainty factor method achieved 80% diagnostic accuracy while the Dempster-Shafer method achieved 85% accuracy. The document concludes by stating that the certainty factor method is better suited than the Dempster-Shafer method for knowledge representation of stroke diseases based
This document discusses using machine learning techniques to predict diabetes. Specifically:
- The authors build several prediction models using machine learning algorithms like logistic regression, KNN, decision trees on a diabetes dataset to classify patients as having diabetes or not.
- They evaluate the performance of the different models using metrics like accuracy, and find that KNN achieved the highest accuracy of 78% on the test data.
- The document also reviews several other studies applying techniques like random forests, support vector machines, convolutional neural networks to the same diabetes prediction task and Pima Indian diabetes dataset.
- The authors conduct their own experiments applying algorithms like logistic regression, KNN, decision trees, random forest, XGBoost to the
APPLY MACHINE LEARNING METHODS TO PREDICT FAILURE OF GLAUCOMA DRAINAGEIJDKP
This study used machine learning methods to predict the failure of glaucoma drainage devices (GDDs) based on patient data. Five classifiers (logistic regression, artificial neural network, random forest, decision tree, and support vector machine) were compared using 165 patient records. Logistic regression and random forest performed best on the small and large datasets respectively. Key predictors of failure identified were race (Black), use of beta-blockers, and glaucoma type (open-angle). Random forest and logistic regression showed potential to predict GDD outcomes based on minimal patient information and could help understand differences between device types.
Breast cancer: Post menopausal endocrine therapyDr. Sumit KUMAR
Breast cancer in postmenopausal women with hormone receptor-positive (HR+) status is a common and complex condition that necessitates a multifaceted approach to management. HR+ breast cancer means that the cancer cells grow in response to hormones such as estrogen and progesterone. This subtype is prevalent among postmenopausal women and typically exhibits a more indolent course compared to other forms of breast cancer, which allows for a variety of treatment options.
Diagnosis and Staging
The diagnosis of HR+ breast cancer begins with clinical evaluation, imaging, and biopsy. Imaging modalities such as mammography, ultrasound, and MRI help in assessing the extent of the disease. Histopathological examination and immunohistochemical staining of the biopsy sample confirm the diagnosis and hormone receptor status by identifying the presence of estrogen receptors (ER) and progesterone receptors (PR) on the tumor cells.
Staging involves determining the size of the tumor (T), the involvement of regional lymph nodes (N), and the presence of distant metastasis (M). The American Joint Committee on Cancer (AJCC) staging system is commonly used. Accurate staging is critical as it guides treatment decisions.
Treatment Options
Endocrine Therapy
Endocrine therapy is the cornerstone of treatment for HR+ breast cancer in postmenopausal women. The primary goal is to reduce the levels of estrogen or block its effects on cancer cells. Commonly used agents include:
Selective Estrogen Receptor Modulators (SERMs): Tamoxifen is a SERM that binds to estrogen receptors, blocking estrogen from stimulating breast cancer cells. It is effective but may have side effects such as increased risk of endometrial cancer and thromboembolic events.
Aromatase Inhibitors (AIs): These drugs, including anastrozole, letrozole, and exemestane, lower estrogen levels by inhibiting the aromatase enzyme, which converts androgens to estrogen in peripheral tissues. AIs are generally preferred in postmenopausal women due to their efficacy and safety profile compared to tamoxifen.
Selective Estrogen Receptor Downregulators (SERDs): Fulvestrant is a SERD that degrades estrogen receptors and is used in cases where resistance to other endocrine therapies develops.
Combination Therapies
Combining endocrine therapy with other treatments enhances efficacy. Examples include:
Endocrine Therapy with CDK4/6 Inhibitors: Palbociclib, ribociclib, and abemaciclib are CDK4/6 inhibitors that, when combined with endocrine therapy, significantly improve progression-free survival in advanced HR+ breast cancer.
Endocrine Therapy with mTOR Inhibitors: Everolimus, an mTOR inhibitor, can be added to endocrine therapy for patients who have developed resistance to aromatase inhibitors.
Chemotherapy
Chemotherapy is generally reserved for patients with high-risk features, such as large tumor size, high-grade histology, or extensive lymph node involvement. Regimens often include anthracyclines and taxanes.
Macquarie Neurosurgery Journal Club 2022 PPTMQ_Library
1) This randomized controlled non-inferiority trial compared full endoscopic discectomy (PTED) to open microdiscectomy (MLD) for sciatica.
2) PTED was found to be non-inferior to open MLD for reducing leg pain, with similar or better outcomes for secondary measures like function and quality of life.
3) PTED had fewer complications than open MLD and allowed for shorter hospital stays.
Genetically Optimized Neural Network for Heart Disease ClassificationIRJET Journal
This document describes a study that uses a genetically optimized neural network to classify heart disease based on patient risk factors. The study collects data on 12 risk factors from 50 patients and encodes the values for use as input to a neural network. The neural network is initially trained using backpropagation, then genetic algorithms are used to optimize the network weights and biases to improve accuracy. Confusion matrices are plotted to evaluate the accuracy of the optimized neural network at classifying patients as having heart disease or not. The approach achieves a classification accuracy of 90% on the test data.
This meta-analysis reviewed 16 randomized controlled trials comparing the effectiveness of motor control exercises (MCE) to other treatments for chronic or recurrent low back pain. The analysis found that MCE was superior to general exercise in reducing both disability in the short, intermediate, and long term, and pain in the short and intermediate term. MCE was also superior to minimal interventions like advice or placebo for both pain and disability outcomes at all time periods. Compared to spinal manual therapy, MCE demonstrated superior results for reducing disability but not pain. The studies varied in quality but provided evidence that MCE can better improve pain and disability for low back pain over the short to long term compared to other common treatments.
A COMPREHENSIVE SYSTEMATIC REVIEW FOR CARDIOVASCULAR DISEASE USING MACHINE LE...gerogepatton
The global upswing in cardiovascular disease (CVD) cases presents a critical challenge. While the
ultimate goal remains elusive, improving CVD prediction accuracy is vital. Machine learning and deep
learning are crucial for decoding complex health data, enhancing cardiac imaging, and predicting disease
outcomes in clinical practice. This systematic literature review meticulously analyses CVD using machine
learning techniques, with a particular emphasis on algorithms for classification and prediction. The metaanalysis covers 343 references from 2020 to November 2023, preceding a thorough examination of 65
selected references. Acknowledging current hurdles in CVD classification methods that impede practical
use, this systematic literature review (SLR) is conducted.
The study provides valuable insights for researchers and healthcare professionals, facilitating the
integration of clinical applications in machine learning settings related to CVD. It also aids in promptly
identifying potential threats and implementing precautionary measures. The study also recognizes
prevalent classical machine learning methods, emphasizing their clinically relevant diagnostic outcomes.
Deliberating on current trends, algorithms, and potential areas for future research offers a comprehensive
insight into the present state of affairs
A COMPREHENSIVE SYSTEMATIC REVIEW FOR CARDIOVASCULAR DISEASE USING MACHINE LE...gerogepatton
The global upswing in cardiovascular disease (CVD) cases presents a critical challenge. While the
ultimate goal remains elusive, improving CVD prediction accuracy is vital. Machine learning and deep
learning are crucial for decoding complex health data, enhancing cardiac imaging, and predicting disease
outcomes in clinical practice. This systematic literature review meticulously analyses CVD using machine
learning techniques, with a particular emphasis on algorithms for classification and prediction. The metaanalysis covers 343 references from 2020 to November 2023, preceding a thorough examination of 65
selected references. Acknowledging current hurdles in CVD classification methods that impede practical
use, this systematic literature review (SLR) is conducted.
The study provides valuable insights for researchers and healthcare professionals, facilitating the
integration of clinical applications in machine learning settings related to CVD. It also aids in promptly
identifying potential threats and implementing precautionary measures. The study also recognizes
prevalent classical machine learning methods, emphasizing their clinically relevant diagnostic outcomes.
Deliberating on current trends, algorithms, and potential areas for future research offers a comprehensive
insight into the present state of affairs.
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30%,. The conclusion that can be drawn is that the approach to the multiclass classification algorithm BTSVM,
OAO-SVM, DDAG-SVM to diagnosis the type or level of coronary heart disease provides better performance, than the binary classification approach.
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2. Kutty: Summary of neurosurgical grading systems
Surgical Neurology International • 2022 • 13(497) | 2
CRANIAL NEUROSURGERY
Glasgow coma scale (GCS)
Professor Teasdale and Jennett first published the GCS in 1974
in the Lancet.[41]
It is used worldwide not only in neurosurgery
but also in many other fields of medicine to assess a patient’s
conscious level and coma. Moreover, it serves as a practical
tool for doctors and nurses to document neurological status
regularly. The GCS has three components to assess; eye
opening – which can be graded from 1 point to 4 points, verbal
response from 1 point to 5 points, and motor responsiveness
from 1 point to 6 points. The sum of each component is used
to calculate an overall score. A minimum score is 3 and a
maximum score is 15 [Table 1]. In neurosurgical practice,
the most important component is the motor score. When
documenting GCS, it is helpful to document the individual
breakdown for each component as well as the overall score, that
is, E4, V5, M6, and GCS 15. If the patient is unable to verbalize,
for example, due to endotracheal intubation or tracheostomy,
then this should be specified when documenting the GCS.
This is often abbreviated in the verbal score as V-T (for
endotracheal tube or tracheostomy). Similarly, if the patient is
dysphasic, then this is written as V-D.
Pediatric GCS
The pediatric GCS [Table 2] is slightly different to the adult
version but assesses the same three components as above. This
scale is used in children below the age of 2 years. Standard
GCS scale can be used for those above the age of 2.[8,25]
Endoscopic third ventriculostomy (ETV) success score
An ETV is a procedure done mainly for obstructive
hydrocephalus (noncommunicating). However, its use is not
limited to this condition. The procedure involves making a
hole in the floor of the third ventricle to allow cerebrospinal
fluid to flow.[13]
ETV success score was proposed by Kulkarni
et al.[28]
and was aimed to estimate the likelihood of ETV
success at 6-month postoperatively. The score takes into
account three components, namely, age of the patient;
etiology; and history of a previous shunt and assigns
percentage points based on this. The points are then added.
A score 80% suggests a high likelihood of success, 50–70%
suggests moderate likelihood of success, and 40% suggests a
low likelihood of success[28]
[Table 3].
House-Brackmann classification for facial nerve palsy
This is a scoring system proposed by Dr. House and
Brackmann in 1985. The purpose of this is to assess the
severity of facial nerve palsy.[23]
It consists of six grades, as
shown in Table 4. In neurosurgical procedures involving
major vestibular schwannomas, avoidance of facial nerve
injury is crucial. This grading system can be utilized to assess
and track the patient’s facial nerve recovery.[40]
Frisen scale for papilledema
The Frisen and modified Frisen scale describes the grade
of optic disk swelling in conditions with raised intracranial
pressure.[17]
These include conditions such as hydrocephalus
and idiopathic intracranial hypertension.[15]
It is particularly
useful in both acute and chronic settings and is one of the
indicators of severity of the above named conditions. It is
also helpful in monitoring disease progression and treatment
outcome[12]
[Table 5].
Grading for diffuse axonal injury (DAI)
Adams et al. described in 1989 a grading system for DAI
based on histology of anatomic distribution of cerebral
Table 1: Glasgow Coma Scale.
Eye opening Verbal response Motor response
Spontaneously (4) Oriented (5) Obeys commands (6)
To voice (3) Confused (4) Localizes to pain (5)
To pain (2) Inappropriate
words (3)
Withdrawal from
pain (4)
no eye opening
(1)
Incomprehensible
sounds (2)
Flexion to pain
(decortication) (3)
No verbal
response (1)
Extension
(decerebration) (2)
No motor response (1)
The numbers in brackets correspond to the points assigned for each area
of the scale.
Table 2: Pediatric Glasgow Coma Scale.
Eye opening Verbal response Motor response
Spontaneously (4) Smiles, oriented to sounds, follows objects, and interacts (5) Moves spontaneously and purposefully (6)
To verbal stimuli (3) Cries, but consolable, inappropriate interactions (4) Withdraws to touch (5)
To pain (2) Inconsistently inconsolable and moaning (3) Withdraws to pain (4)
No eye opening (1) Inconsolable and agitated (2)
No verbal response (1)
Abnormal flexion to pain (3)
Extension to pain (2)
No motor response (1)
The numbers in brackets correspond to the points assigned for each area of the scale.
3. Kutty: Summary of neurosurgical grading systems
Surgical Neurology International • 2022 • 13(497) | 3
hemorrhage.[2]
There are three stages, 1–3, with worse
outcome associated with higher grade. MRI classification
proposed by Gentry[20]
is shown in Table 6.
The Glasgow outcome scale (GOS)
The GOS aims to assess outcome after head injury.[26]
It
consists of five grades. This may assist in assessing the patient’s
requirements, such as rehabilitation needs post brain injury.
An extended scale called the ‘GOS extended’ also exists. The
GOS scale is shown in Table 7.
VASCULAR NEUROSURGERY
Grading systems for subarachnoid hemorrhage (SAH)
There are three main grading systems used for aneurysmal
SAH. The Hunt and Hess classification quantifies the severity
of SAH to predict mortality.[24]
It is based solely on clinical
examination findings. The second grading system is the
World Federation of Neurological Surgeons system, which
is based on GCS and the presence or absence of neurologic
deficits and aims to predict outcome based on this.[14]
The
third system is the Fisher grade and modified Fisher grade,
which looks at the distribution and volume of blood on
CT brain scan images and aims to predict the occurrence
of cerebral vasospasm.[16,18]
The three grading systems are
highlighted in Table 8.
Spetzler-Martin grade for arteriovenous malformation
(AVM)
The Spetzler-Martin grading system [Table 9] aids in
estimating the risk of surgical resection of cerebral AVMs.[38]
This is based on three areas, eloquence of surrounding brain,
Table 3: Endoscopic third ventriculostomy success score.
Category Score
Age • 1 month=0
• 1–6 months=10
• 6–12 months=30
• 1–10 years=40
• ≥10 years=50
Etiology • Postinfectious=0
•
Myelomeningocele, intraventricular hemorrhage,
or nontectal brain tumor=20
•
Aqueductal stenosis, tectal tumor, or other
etiology=30
• Previous shunt=0
• No previous shunt=10
Table 4: House‑Brackmann classification for facial nerve palsy.
Grade Description
1. Normal
2. Mild dysfunction Slight weakness and normal symmetry
at rest
3. Moderate
dysfunction
(Obvious but not disfiguring weakness
with synkinesis, normal symmetry at
rest) complete eye closure with maximal
effort, good forehead movement
4.
Moderately severe
dysfunction
(Obvious and disfiguring asymmetry,
significant synkinesis) incomplete eye
closure, moderate forehead movement
5. Severe dysfunction Barely perceptible motion
6. Total paralysis No motion
Table 5: Frisen scale for papilledema.
Grade Description
0 Normal optic disk
1 Minimal papilledema – Subtle C shaped halo of disk
edema with a normal temporal disk margin
2 Low degree papilledema – Circumferential halo of disk
edema
3 Moderate papilledema – Obscuration of one or more
segments of the major blood vessels leaving the disk
4 Marked papilledema – Partial obstruction of a segment
of major blood vessel on the disk
5 Severe papilledema – Partial or total obstruction of all
the blood vessels on the disk
Table 6: MRI grading system for DAI.
Stage Description
Stage 1 Lobar: diffuse axonal injury lesions confined to the
lobar white matter, especially gray‑white matter
junction
Stage 2 Callosal: diffuse axonal injury lesions in the corpus
callosum, almost invariably in addition to the lobar
white matter
Stage 3 Brainstem: diffuse axonal injury lesions in the
brainstem, almost invariably in addition to the lobar
white matter and corpus callosum
Table 7: Glasgow outcome scale.
Grade Description
1‑Death No recovery of consciousness – Death
2‑Persistent
vegetative state
Severe damage with prolonged state of
unresponsiveness and a lack of higher
mental functions
3‑Severe disability Severe injury with permanent need for
help with activities of daily living
4‑Moderate disability Does not require assistance in everyday
life, employment is possible but may
require special equipment
5‑Low disability Light damage with minor neurological
and psychological deficits
4. Kutty: Summary of neurosurgical grading systems
Surgical Neurology International • 2022 • 13(497) | 4
presence of deep venous drainage, and the size of the nidus.
Each area is given a score. The sum of the allocated points
forms the grade. Higher grades (Grades 4 and 5) are generally
unsuitable for surgical management.
Alberta stroke program early CT score (ASPECTS)
The ASPECTS is a scoring system based on 10 points and is
used to predict outcome of middle cerebral artery stroke.[6]
A baseline score of 10 is assigned and points are deducted
by 1 point for each of the following areas affected: caudate,
putamen, internal capsule, and insular cortex, M1-M6
territories (1 point assigned to each of the M1-M6 territories).
Lower scores are associated with worse outcome.
The intracerebral hemorrhage score (ICH)
The ICH score is used to help grade patients with ICH.[21]
The scale takes into account the patients’ GCS, ICH volume,
and intraventricular hemorrhage (IVH), whether or not the
origin of the ICH is infratentorial and the age of the patient.
Higher scores are associated with an increase in 30-day
mortality. The score is graded 0–6 points. This is shown in
Table 10.
Papile-Burstein classification for IVH
Another useful and commonly used grading system in
neurosurgical patients is a grading system used for IVH
proposed by Papile et al.[33]
This is known as the Papile-
Burstein classification for IVH. It consists of four grades,
with higher grades associated with a worse prognosis. It is
based on CT scan findings. Table 11 shows this.
Classification systems for dural arteriovenous fistula (DAVF)
Borden et al. classification for DAVF was proposed in
1995.[7]
It describes different types of DAVF which are
grouped into three types. They are grouped based on their
cortical venous drainage and their location. Types 2 and 3
tend to have a high risk of bleeding and causing problems
Table 8: Grading systems for aneurysmal SAH.
Grade Hunt and Hess grade World Federation of
Neurological Surgeons grade
Modified Fisher grade
1 Mild headache, alert and oriented, and minimal
(if any) neck stiffness
GCS 15, no motor deficit Focal or diffuse thin SAH and no
intraventricular hemorrhage
2 Full neck stiffness, moderate‑severe headache, alert
and oriented, and no neurodeficit (besides CN palsy)
GCS 13–14, no motor deficit Focal or diffuse thin SAH and with
intraventricular hemorrhage
3 Lethargy or confusion and mild focal neurological
deficits
GCS 13–14, with motor deficit Thick SAH and no intraventricular
hemorrhage
4 Stupor, moderate‑to‑severe hemiparesis, possible
early decerebrate rigidity, and vegetative disturbances
GCS 7–12, motor deficit
present or absent
Thick SAH and with intraventricular
hemorrhage
5 Deep coma, decerebrate rigidity, and moribund GCS 3–6, motor deficit present
or absent
GCS: Glasgow Coma Scale
Table 9: Spetzler‑Martin grade for arteriovenous malformation.
Eloquence of
surrounding brain
Presence of deep
draining veins
Size of the nidus
Eloquent site – 1 point Present 1 point 3 cm – 1 point
Non eloquent site –
0 point
Absent 0 point 3–6 cm – 2 points
6 cm – 3 points
Table 10: The ICH score.
Category Points score
GCS 3–4 – 2 points
5–12 – 1 point
13–15 – 0 points
ICH volume /=30 cm3
– 1 point 30 cm3
– 0 points
IVH Yes – 1 point no – 0 points
Infratentorial
origin of ICH
Yes – 1 point no – 0 points
Age More than or equal to 80 years – 1 point
80 years – 0 point
ICH: Intracerebral hemorrhage score, GCS: Glasgow Coma Scale
Table 11: Papile‑Burstein classification for IVH.
Grade Description
1 Subependymal germinal matrix hemorrhage
2 Hemorrhage extension into the ventricles – 50% of
the ventricle filled
3 Hemorrhage extension into the ventricles – more than
50% of the ventricle filled
4 IVH with parenchymal extension – associated with
periventricular venous infarction
IVH: Intraventricular hemorrhage
5. Kutty: Summary of neurosurgical grading systems
Surgical Neurology International • 2022 • 13(497) | 5
such as neurologic deficit,[44]
whereas Type 1 DAVF generally
behave less aggressively.[39]
The Cognard classification system
was proposed in 2016 and has five grades and importantly
takes into account the presence of venous ectasia and the
direction of blood flow.[10]
Both grading systems are depicted
in Table 12.
NIH stroke scale/score (NIHSS)
The NIHSS aims to describe the severity of ischemic stroke,
with a score of 0–42 being assigned to patients. Higher scores
are associated with greater stroke severity.[29]
It is also useful
for predicting outcome after ischemic stroke. For every
increase in 1 point, the likelihood of excellent outcome was
decreased at 7 days by 24% and by 17% at 3 months.[1]
A score
of 1–4 is classified as a minor stroke and 5–15 is a moderate
stroke. Scores above 21 are classified as a severe stroke. The
scoring system is highlighted in Table 13.
SPINAL NEUROSURGERY AND
MISCELLANEOUS
American spinal injury association (ASIA) impairment
scale and grade in spinal cord injury
This is a grading system consisting of five grades, allowing
clinicians to assess the severity of spinal cord injury.[5]
It also
aids in determining rehabilitation requirements and potential
for recovery/prognosis. It involves conducting a series of
sensory and motor function tests based on a chart proposed
by the ASIA.[4]
After completing the chart, points are totaled.
A maximum of 112 points can be given. The grading system
is shown in Table 14.[5]
Complete ASIA spinal cord injury
chart is not included in this article.
Medical research council grading system for muscle strength
This is a commonly used grading system in neurosurgical
practice to assess patient’s muscle strength. Much like the
GCS, it serves as a reliable tool for nurses and doctors to
utilize and regularly document the clinical status of the
patient. In addition, it can guide treatment, assess response to
Table 12: Classification systems for dural arteriovenous fistula.
Cognard classification Borden classification
Type I – drainage into dural venous
sinus only, with normal antegrade flow
Type II A – drainage into dural venous
sinus only, with retrograde flow
Type 1 – Drainage into
meningeal veins, spinal
epidural veins, or into a
dural venous sinus only
Type II B – Drainage into dural venous
sinus, with antegrade flow and cortical
venous drainage
Type II a+b – Drainage into dural
venous sinus with retrograde flow and
cortical venous drainage
Type 2 – Drainage into
meningeal veins, spinal
epidural veins, or into a
dural venous sinus and
cortical venous drainage
Type III – Venous drainage into
subarachnoid veins – cortical venous
drainage only
Type IV – Type III with venous ectasia
of the draining subarachnoid veins
Type V – drainage into spinal
perimedullary veins
Type 3 – Direct
drainage into
subarachnoid veins
(cortical venous
drainage only)
Table 13: NIH stroke scale/score.
Area assessed Scale
Level of consciousness 0. Alert
1. Drowsy
2. Obtunded
3. Coma/unresponsive
Orientation questions 0. answers both questions correctly
1. Answers one correctly
2. Answers neither correctly
Response to commands 0. Performs both tasks correctly
1. Performs one task correctly
2. Answers neither
Gaze 0. Normal horizontal movements
1. Partial gaze palsy
2. Complete gaze palsy
Visual field 0. No visual field defect
1. Partial hemianopia
2. Complete hemianopia
3. Bilateral hemianopia
Facial movement 0. Normal
1. Minor facial weakness
2. Partial facial weakness
3. Complete unilateral palsy
Motor function arm
(left and right)
0. No drift
1. Drift before 10 s
2. Falls before 10 s
3. No effort against gravity
4. No movement
Motor function leg
(left and right)
0. No drift
1. Drift before 5 s
2. Falls before 5 s
3. No effort against gravity
4. No movement
Limb ataxia 0. No ataxia
1. Ataxia in one limb
2. Ataxia in two limbs
Sensory 0. No sensory loss
1. Mild sensory loss
2. Severe sensory loss
Language 0. Normal
1. Mild aphasia
2. Severe aphasia
3. Mute or global aphasia
Articulation 0. Normal
1. Mild dysarthria
2. Severe dysarthria
Extinction or inattention 0.Absent
1. Mild loss (one sensory modality lost)]
2. Severe loss (two modalities lost)
6. Kutty: Summary of neurosurgical grading systems
Surgical Neurology International • 2022 • 13(497) | 6
treatment, and aid in prognostication. It consists of six grades
(0–5), as depicted in Table 15.[11]
Karnofsky clinical performance status
The Karnofsky clinical performance status is useful
in assessing patient’s functional status and suitability
for treatment such as chemotherapy.[27]
It also aids in
determining prognosis and response to treatment, especially
in chronic disease.[27,31]
Patients are given a score between 0
and 100, 100 being the best possible score and 0 being the
worst [Table 16].
Modified Rankin scale
The modified Rankin scale is used to assess outcome after
stroke.Itisalsousefultoassessrehabilitationrequirements[34,42]
[Table 17]. Over the years, the mRS has evolved as the
primary outcome measure for nearly all acute stroke trials,
even though it is considered as a single-item handicap scale.
Neurosurgical diagnoses are complex and single-item scales
might not be able to capture the depth of the clinical problem.
Likewise, such scales are notorious for multiple and variable
interpretations based on the person attempting the scoring
in diverse settings. Appropriate statistical tests to analyze the
scale results are important if study results using this scale are
to be implemented in practice guidelines.[35]
Simpson grade of meningioma resection
The Simpson grade for meningioma resection aims to
correlate the degree of surgical resection with the likelihood
Table 14: ASIA impairment scale for spinal cord injury.
Grade Impairment
A Complete No motor or sensory function preserved in the
sacral segments S4‑S5
B Incomplete Sensory, but not motor function is preserved
below the neurologic level and includes the sacral
segments S4‑S5
C Incomplete Motor function preserved below the neurologic
level, and more than half of key muscles below
the neurologic level have a muscle grade 3
D Incomplete Motor function preserved below the neurologic
level, and at least half of the key muscles below the
neurologic level have a muscle grade of 3 or more
E Normal Motor and sensory function are normal.
ASIA: American Spinal Injury Association
Table 15: Medical Research Council grading for muscle strength.
Grade Description
0 No visible muscle contraction
1 Flicker of contraction in the muscle
2 Movement with gravity eliminated
3 Movement against gravity
4 Movement against gravity and resistance
5 Normal power
Table 16: Karnofsky clinical performance status.
Score Health status
100 Normal; no complaints; no evidence of disease
90 Able to carry on normal activity; minor signs or
symptoms of disease
80 Normal activity with effort; some signs or symptoms of
disease
70 Cares for self; unable to carry on normal activity or to do
active work
60 Requires occasional assistance, but is able to care for
most of their personal needs
50 Requires considerable assistance and frequent medical care.
40 Disabled; requires special care and assistance
30 Severely disabled; hospital admission is indicated
although death not imminent
20 Very sick; hospital admission necessary; active
supportive treatment necessary
10 Moribund, fatal processes progressing rapidly
0 Dead
Table 17: Modified Rankin score.
Grade Description
0 No symptoms
1 No significant disability: has symptoms, but able to
carry out all usual duties and activities
2 Slight disability: unable to carry out all previous activities,
but able to look after own affairs without assistance
3 Moderate disability: requiring some help, but able to
walk without assistance
4 Moderately severe disability: unable to walk without
assistance, and unable to attend own bodily needs
without assistance.
5 Severe disability: bedridden, incontinent, and requiring
constant nursing care and attention
6 Dead
Table 18: Simpson grade of meningioma resection.
Grade Description
1 Macroscopically complete removal of tumor, with
excision of its dural attachment, and of any abnormal
bone. Includes resection of venous sinus if involved.
2 Macroscopically complete removal of tumor and its
visible extensions with coagulation of its dural attachment
3 Macroscopically complete removal of the intradural
tumor, without resection or coagulation of its dural
attachment or its extradural extensions
4 Partial removal, leaving intradural tumor in situ.
5 Simple decompression, with or without biopsy
7. Kutty: Summary of neurosurgical grading systems
Surgical Neurology International • 2022 • 13(497) | 7
of meningioma recurrence.[32,37]
There are also other factors,
which play a role in determining risk of recurrence. Table 18
gives the grades of resection. The Simpson grade for
meningioma was previously considered as the gold standard
for defining the surgical extent of resection for the WHO
Grade 1 meningioma.[9]
The grade is based on intraoperative
“eyeballing” of resection, which cannot be considered
accurate. This has unearthed many controversies including
rendering many previous outcome studies based on this
grading system redundant. The technological advancements
in the field of neurosurgery have diminished the value of this
scale for prognostication of recurrence after meningioma
resection. Many recent articles have urged to abandon this
system in clinical practice but preserve its original message.[9]
Anderson and D’Alonzo classification of odontoid process
fracture
Anderson and D’Alonzo described an important and
commonly used classification system for odontoid process
fractures.[3]
This classification is shown in Table 19. There are
three types of fractures, and this helps guide management
of these fractures. Type 2 fractures have a higher a rate of
nonunion and are usually unstable.[3]
Galassi classification of arachnoid cyst
Galassi et al. described a classification system for middle
cranial fossa arachnoid cysts in 1982.[19]
It consists of three
types of cysts based on radiological criteria. The classification
is utilized to guide surgical management of these cysts. They
are highlighted in Table 20.
DISCUSSION
There are a vast number of common and obscure grading
systems in clinical and research practice. Sifting out the most
relevant one for the clinical scenario is not an easy task for
clinicians. Only by incorporating them into routine practice
that one realizes that there is no single “gold standard” scale,
rather many scales albeit with different properties. Based
on your clinical requirement, one should choose the most
appropriate grading system. The grading system should have
the ability to be incorporated seamlessly into routine clinical
use. Moreover, it should be reliable, repeatable, and provide a
valid measurement for the specific outcome.
Over the past two decades, there has been an explosion of
psychometric methods using statistical techniques in an
attempt to provide strength to the measurements obtained
from grading systems.[36]
Measurements or scores obtained
from scales are dependent on the scale itself as well as the
subjects. These nonlinear variables can be transformed
into interval measures, which give a more objective result,
negating many problems such as underestimation.
Tremendous amount of work and expertise have gone into
the creation of grading systems over the years and this
article is an attempt to acknowledge these contributions to
health measurements. In the process, I would like to draw
the attention of clinicians to the nuances, limitations, and
benefits of such systems, as pointed out by Massof[30]
about
two decades ago. The cardinal point to remember when we
use these systems is to understand that “observations are
always ordinal: measurements, however, must be interval” as
aptly titled in their article by Wright and Linacre.[43]
CONCLUSION
Conclusions drawn from the various measurements used
in grading systems dictate patient care. Some of them
such as GCS scoring have immediate outcomes, whereas
many disability ratings have significant long-term impact
including health-care expenditure, clinical guidelines, and
even policy-making. Therefore, it is crucial that those of
us using them are aware of the validity and quality of the
rating scales used in clinical settings. Many variables such
as patient perspectives and quality of life indices studies
do need stringent measurement criteria as they can change
some clinical practices. These facts have been pointed out
by studies of rating scales in the field of neurology, where
the choice of rating scales had affected the clinical course
of diseases such as multiple sclerosis.[22]
Rigorous statistical
analyses of the outcomes from grading systems are not a
Table 19: Anderson and D’Alonzo classification of odontoid
process fracture.
Classification Description
Type 1 Fracture through the tip of the odontoid
process, associated with apical ligament avulsion
– Usually stable
Type 2 Fracture through the body or base of the peg –
Usually unstable
Type 3 Body of C2 involved with comminuted
fragments – Unstable fracture
Table 20: Galassi classification of arachnoid cyst.
Type Description
1 Small and limited to anterior part of the middle cranial
fossa – communicates freely with the subarachnoid space
2 Extend along the Sylvian fissure and can displace
the temporal lobe – slow communication with the
subarachnoid space
3 Large cyst, occupies whole of middle cranial fossa,
displaces multiple lobes, and presence of midline shift.
Little communication with the subarachnoid space.
8. Kutty: Summary of neurosurgical grading systems
Surgical Neurology International • 2022 • 13(497) | 8
solution for the inherent issues with the scale itself. The
above listed grading systems are some of the most commonly
used in neurosurgical practice. As mentioned before, there
are many other useful grading systems used in various areas
of neurosurgery not included in this article. I hope that this
summary will benefit the neurosurgical community and
wider audience and serve as a handy reference tool.
Declaration of patient consent
Patient’s consent not required as there are no patients in this
study.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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How to cite this article: Kutty S. A summary of common grading systems
used in neurosurgical practice. Surg Neurol Int 2022;13:497.
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