This document discusses the differences between clinical trials and health outcomes research. Clinical trials use homogeneous samples, surrogate endpoints, and focus on a single outcome. They are also typically underpowered for rare events. Health outcomes research uses heterogeneous data from the general population to examine multiple real endpoints simultaneously. It has larger samples and data that allow analysis of rare occurrences. Predictive modeling is better suited than traditional statistical methods for analyzing heterogeneous health outcomes data due to relaxed assumptions like normality.
Lecture on causal inference to the pediatric hematology/oncology fellows at Texas Children's hospital as part of their Biostatistics for Busy Clinicians lecture seriers.
Statistics For Data Analytics - Multiple & logistic regression Shrikant Samarth
Task: To build multiple regression and logistic regression models on appropriate data.
Approach: A general topic was selected first after which the data was downloaded from the source keeping the restrictions in mind and then cleaned in R. Then the multiple regression and logistic regression models were built using IBM SPSS and the outputs were interpreted. The dependent variable was life expectancy and the independent variables were Age-standardized Mortality-Communicable”, “Age-standardized Mortality-Cardiovascular Disease and Diabetes".
Findings: Multipleregression - analysis was conducted to make sure normality, linearity, multi-collinearity, independence of errors and homoscedasticity were not violated. Statistically, the score of Life expectancy at age 60, 퐹(2,102) = 39.474 푅2 = .436, 푝 < 0.0005
Logistic Regression: Result shows 58.9% (Cox & Snell R-Square) and 80.1% (Nagelkerke R-Square) of the variance and gives 92.4% of correctly classified countries. The two indicating factors made a remarkable commitment to the model. Also, the model predicts the increase in “Mortality-Cardiovascular/Diabetes” and “Mortality rate cause by Communicable” variables is the cause of a decrease in Life Expectancy in a country.
Tools: IBM SPSS
In order to understand medical statistics, you have to learn the very basic concepts as mean, median, and standard deviation. interpretation and understanding of clinical study results depends mainly on statistical background.
Lecture on causal inference to the pediatric hematology/oncology fellows at Texas Children's hospital as part of their Biostatistics for Busy Clinicians lecture seriers.
Statistics For Data Analytics - Multiple & logistic regression Shrikant Samarth
Task: To build multiple regression and logistic regression models on appropriate data.
Approach: A general topic was selected first after which the data was downloaded from the source keeping the restrictions in mind and then cleaned in R. Then the multiple regression and logistic regression models were built using IBM SPSS and the outputs were interpreted. The dependent variable was life expectancy and the independent variables were Age-standardized Mortality-Communicable”, “Age-standardized Mortality-Cardiovascular Disease and Diabetes".
Findings: Multipleregression - analysis was conducted to make sure normality, linearity, multi-collinearity, independence of errors and homoscedasticity were not violated. Statistically, the score of Life expectancy at age 60, 퐹(2,102) = 39.474 푅2 = .436, 푝 < 0.0005
Logistic Regression: Result shows 58.9% (Cox & Snell R-Square) and 80.1% (Nagelkerke R-Square) of the variance and gives 92.4% of correctly classified countries. The two indicating factors made a remarkable commitment to the model. Also, the model predicts the increase in “Mortality-Cardiovascular/Diabetes” and “Mortality rate cause by Communicable” variables is the cause of a decrease in Life Expectancy in a country.
Tools: IBM SPSS
In order to understand medical statistics, you have to learn the very basic concepts as mean, median, and standard deviation. interpretation and understanding of clinical study results depends mainly on statistical background.
Webinar slides how to reduce sample size ethically and responsiblynQuery
[Webinar] How to reduce sample size...ethically and responsibly | In this free webinar, you will learn various design strategies to help reduce the sample size of your study in an ethical and responsible manner. Practical examples will be used throughout.
Due to the advancements in various data acquisition and storage technologies, different disciplines have attained the ability to not only accumulate a wide variety of data but also to monitor observations over longer time periods. In many real-world applications, the primary objective of monitoring these observations is to estimate when a particular event of interest will occur in the future. One of the major difficulties in handling such problem is the presence of censoring, i.e., the event of interests is unobservable in some instance which is either because of time limitation or losing track. Due to censoring, standard statistical and machine learning based predictive models cannot readily be applied to analyze the data. An important subfield of statistics called survival analysis provides different mechanisms to handle such censored data problems. In addition to the presence of censoring, such time-to-event data also encounters several other research challenges such as instance/feature correlations, high-dimensionality, temporal dependencies, and difficulty in acquiring sufficient event data in a reasonable amount of time. To tackle such practical concerns, the data mining and machine learning communities have started to develop more sophisticated and effective algorithms that either complement or compete with the traditional statistical methods in survival analysis. In spite of the importance of this problem and relevance to real-world applications, this research topic is scattered across various disciplines. In this tutorial, we will provide a comprehensive and structured overview of both statistical and machine learning based survival analysis methods along with different applications. We will also discuss the commonly used evaluation metrics and other related topics. The material will be coherently organized and presented to help the audience get a clear picture of both the fundamentals and the state-of-the-art techniques.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
Non-inferiority and Equivalence Study design considerations and sample sizenQuery
About the webinar
This webinar examines the role of non-inferiority and equivalence in study design
In this free webinar, you will learn about:
-Regulatory information on this type of study design
-Considerations for study design and your sample size
-Practical worked examples of
--Non-inferiority Testing
--Equivalence Testing
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Watch the video at: https://www.statsols.com/webinars
Sample size for survival analysis - a guide to planning successful clinical t...nQuery
Determining the appropriate number of events needed for survival analysis is a complex task as study planners try to predict what sample size will be needed after accounting for the complications of unequal follow-up, drop-out and treatment crossover.
The statistical, logistical and ethical considerations all complicate life for biostatisticians as issues to balance in planning a survival analysis. However, this complexity has created a need for new analyses and procedures to help the planning process for survival analysis trials.
The wider move from fixed to flexible designs has opened up opportunities for advanced methods such as adaptive design and Bayesian analysis to help deal with the unique complications of planning for survival data but these methods have their own complications that need to be explored too.
2020 trends in biostatistics what you should know about study design - slid...nQuery
2020 Trends In Biostatistics - What you should know about study design.
In this free webinar you will learn about:
-Adaptive designs in confirmatory trials
-Using external data in study planning
-Innovative designs in early-stage trials
To watch the full webinar:
https://www.statsols.com/webinar/2020-trends-in-biostatistics-what-you-should-know-about-study-design
Clinical trials: quo vadis in the age of covid?Stephen Senn
A discussion of the role of clinical trials in the age of COVID. My contribution to the phastar 2020 life sciences summit https://phastar.com/phastar-life-science-summit
Innovative Sample Size Methods For Clinical Trials nQuery
"Innovative Sample Size Methods for Clinical Trials" is hosted to coincide with the Spring 2018 update to nQuery - The leading Sample Size Software.
Hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - you'll learn about the benefits of a range of procedures and how you can implement them into your work:
1) Dose-escalation with the Bayesian Continual Reassessment Method
CRM is a growing alternative to the 3+3 method for Phase I trials finding the Maximum Tolerated Dose (MTD).
See how researchers can overcome 3+3 drawbacks to easily find the required sample size for this beneficial alternative for finding the MTD.
2) Bayesian Assurance with Survival Example
This Bayesian alternative to power has experienced a rapid rise in interest and application from researchers.
See how Assurance is being used by researchers to discover the true “probability of success” of a trial.
3) Mendelian Randomization
Mendelian randomization (MR) is a method that allows testing of a causal effect from observational data in the presence of confounding factors.
However, in order to design efficient Mendelian randomization studies, it is essential to calculate the appropriate sample sizes required. We demonstrate what to do to achieve this.
4) Negative Binomial Distribution
Negative binomial model has been increasingly used to model the count data. One of the challenges of applying negative binomial model in clinical trial design is the sample size estimation.
We demonstrate how best to determine the appropriate sample size in the presence of challenges such as unequal follow-up or dispersion.
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Webinar slides how to reduce sample size ethically and responsiblynQuery
[Webinar] How to reduce sample size...ethically and responsibly | In this free webinar, you will learn various design strategies to help reduce the sample size of your study in an ethical and responsible manner. Practical examples will be used throughout.
Due to the advancements in various data acquisition and storage technologies, different disciplines have attained the ability to not only accumulate a wide variety of data but also to monitor observations over longer time periods. In many real-world applications, the primary objective of monitoring these observations is to estimate when a particular event of interest will occur in the future. One of the major difficulties in handling such problem is the presence of censoring, i.e., the event of interests is unobservable in some instance which is either because of time limitation or losing track. Due to censoring, standard statistical and machine learning based predictive models cannot readily be applied to analyze the data. An important subfield of statistics called survival analysis provides different mechanisms to handle such censored data problems. In addition to the presence of censoring, such time-to-event data also encounters several other research challenges such as instance/feature correlations, high-dimensionality, temporal dependencies, and difficulty in acquiring sufficient event data in a reasonable amount of time. To tackle such practical concerns, the data mining and machine learning communities have started to develop more sophisticated and effective algorithms that either complement or compete with the traditional statistical methods in survival analysis. In spite of the importance of this problem and relevance to real-world applications, this research topic is scattered across various disciplines. In this tutorial, we will provide a comprehensive and structured overview of both statistical and machine learning based survival analysis methods along with different applications. We will also discuss the commonly used evaluation metrics and other related topics. The material will be coherently organized and presented to help the audience get a clear picture of both the fundamentals and the state-of-the-art techniques.
Minimizing Risk In Phase II and III Sample Size CalculationnQuery
[ Watch Webinar: http://bit.ly/2thIgmi ]. In this free webinar, Head of Statistics at Statsols, Ronan Fitzpatrick, addresses the issues of reducing risk in Phase II/III sample size calculations. Topics covered will include:
Sample Size Determination For Different Trial Designs
Bayesian Sample Size Determination
Sample Size For Survival Analysis
& more
Non-inferiority and Equivalence Study design considerations and sample sizenQuery
About the webinar
This webinar examines the role of non-inferiority and equivalence in study design
In this free webinar, you will learn about:
-Regulatory information on this type of study design
-Considerations for study design and your sample size
-Practical worked examples of
--Non-inferiority Testing
--Equivalence Testing
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Watch the video at: https://www.statsols.com/webinars
Sample size for survival analysis - a guide to planning successful clinical t...nQuery
Determining the appropriate number of events needed for survival analysis is a complex task as study planners try to predict what sample size will be needed after accounting for the complications of unequal follow-up, drop-out and treatment crossover.
The statistical, logistical and ethical considerations all complicate life for biostatisticians as issues to balance in planning a survival analysis. However, this complexity has created a need for new analyses and procedures to help the planning process for survival analysis trials.
The wider move from fixed to flexible designs has opened up opportunities for advanced methods such as adaptive design and Bayesian analysis to help deal with the unique complications of planning for survival data but these methods have their own complications that need to be explored too.
2020 trends in biostatistics what you should know about study design - slid...nQuery
2020 Trends In Biostatistics - What you should know about study design.
In this free webinar you will learn about:
-Adaptive designs in confirmatory trials
-Using external data in study planning
-Innovative designs in early-stage trials
To watch the full webinar:
https://www.statsols.com/webinar/2020-trends-in-biostatistics-what-you-should-know-about-study-design
Clinical trials: quo vadis in the age of covid?Stephen Senn
A discussion of the role of clinical trials in the age of COVID. My contribution to the phastar 2020 life sciences summit https://phastar.com/phastar-life-science-summit
Innovative Sample Size Methods For Clinical Trials nQuery
"Innovative Sample Size Methods for Clinical Trials" is hosted to coincide with the Spring 2018 update to nQuery - The leading Sample Size Software.
Hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - you'll learn about the benefits of a range of procedures and how you can implement them into your work:
1) Dose-escalation with the Bayesian Continual Reassessment Method
CRM is a growing alternative to the 3+3 method for Phase I trials finding the Maximum Tolerated Dose (MTD).
See how researchers can overcome 3+3 drawbacks to easily find the required sample size for this beneficial alternative for finding the MTD.
2) Bayesian Assurance with Survival Example
This Bayesian alternative to power has experienced a rapid rise in interest and application from researchers.
See how Assurance is being used by researchers to discover the true “probability of success” of a trial.
3) Mendelian Randomization
Mendelian randomization (MR) is a method that allows testing of a causal effect from observational data in the presence of confounding factors.
However, in order to design efficient Mendelian randomization studies, it is essential to calculate the appropriate sample sizes required. We demonstrate what to do to achieve this.
4) Negative Binomial Distribution
Negative binomial model has been increasingly used to model the count data. One of the challenges of applying negative binomial model in clinical trial design is the sample size estimation.
We demonstrate how best to determine the appropriate sample size in the presence of challenges such as unequal follow-up or dispersion.
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Understanding SAS Data Step Processingguest2160992
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Practical Methods To Overcome Sample Size ChallengesnQuery
Watch the video at: https://www.statsols.com/webinars/practical-methods-to-overcome-sample-size-challenges
In this webinar hosted by Ronan Fitzpatrick - Head of Statistics and nQuery Lead Researcher at Statsols - we will examine some of the most common practical challenges you will experience while calculating sample size for your study. These challenges will be split into two categories:
1. Overcoming Sample Size Calculation Challenges
(Survival Analysis Example)
We will examine practical methods to overcome common sample size calculation issues by focusing in on one of the more complex areas for sample size determination; Survival analysis. We will cover difficulties and potential issues surrounding challenges such as:
Drop Out: How to deal with expected dropouts or censoring. We compare the simple loss-to-follow-up method and integrating a dropout process into the sample size model?
Planning Uncertainty: How best to deal with the inevitable uncertainty at the planning stage? We examine how best to apply a sensitivity analysis and Bayesian approaches to explore the uncertainty in your sample size calculations.
Choosing the Effect Size: Various approaches and interpretations exist for how to find the effect size value. We examine those contrasting interpretations and determine the best method and also how to deal with parameterization options.
2. Overcoming Study Design Challenges
(Vaccine Efficacy Example)
The Randomised Controlled Trial (RCT) is considered the gold standard in trial design in drug development. However, there are often practical impediments which mean that adjustments or pragmatic approaches are needed for some trials and studies.
We will examine practical methods how to overcome common study design challenges and how these affect your sample size calculations. In this webinar, we will use common issues in vaccine study design to examine difficulties surrounding issues such as:
Case-Control Analysis: We will examine how to deal with study constraints and how to deal with analyses done during an observational study.
Alternative Randomization Methods: How best to address randomization in your vaccine trial design when full randomization is difficult, expensive or impractical. We examine how sample size calculations are affected with cluster or Mendelian randomization.
Rare Events: How does an outcome being rare affect the types of study design and statistical methods chosen in your study.
Extending A Trial’s Design Case Studies Of Dealing With Study Design IssuesnQuery
About the webinar
As trials increase in complexity and scope, there is a requirement for trial designs to reflect this.
From dealing with non-proportional hazards in survival analysis to dealing with cluster randomization, we examine how to deal with study design issues of complex trials.
In this free webinar, you will learn about:
Dealing with study design issues
Practical worked examples of
Non-proportional Hazards
Cluster Randomization
Three Armed Trials
Non-proportional Hazards
Non-proportional hazards and complex survival curves have become of increasing interest, due to being commonly seen in immunotherapy development. This has led to interest in assessing the robustness of standard methods and alternative methods that better adapt to deviations.
In this webinar, we look at methods proposed for complex survival curves and the weighted log-rank test as a candidate model to deal with a delayed survival effect.
Cluster Randomization
Cluster-randomized designs are often adopted when there is a high risk of contamination if cluster members were randomized individually. Stepped-wedge designs are useful in cases where it is difficult to apply a particular treatment to half of the clusters at the same time.
In this webinar, we introduce cluster randomization and stepped-wedge designs to provide an insight into the requirements of more complex randomization schedules.
Three Armed Trials
Non-inferiority testing is a common hypothesis test in the development of generic medicine and medical devices. The most common design compares the proposed non-inferior treatment to the standard treatment alone but this leaves uncertain if the treatment effect is the same as from previous studies. This “assay sensitivity” problem can be resolved by using a three arm trial which includes placebo alongside the new and reference treatments for direct comparison.
In this webinar we show a complete testing approach to this gold standard design and how to find the appropriate allocation and sample size for this study.
Duration - 60 minutes
Speaker: Ronan Fitzpatrick, Head of Statistics, Statsols
Probability Models for Estimating Haplotype Frequencies and Bayesian Survival...Université de Dschang
M. Kum Cletus Kwa a soutenu une thèse de Doctorat/Phd en mathématiques ce 14 juin 2016 à l'Université de Dschang. Le jury lui a décerné à l'issue des échanges la mention très honorable.
Biostatistics - the application of statistical methods in the life sciences including medicine, pharmacy, and agriculture.
An understanding is needed in practice issues requiring sound decisions.
Statistics is a decision science.
Biostatistics therefore deals with data.
Biostatistics is the science of obtaining, analyzing and interpreting data in order to understand and improve human health.
Applications of Biostatistics
Design and analysis of clinical trials
Quality control of pharmaceuticals
Pharmacy practice research
Public health, including epidemiology
Genomics and population genetics
Ecology
Biological sequence analysis
Bioinformatics etc.
ISCB 2023 Sources of uncertainty b.pptxBenVanCalster
This talk gives an overview and illustration of various sources of uncertainty when developing clinical risk prediction models: aleatory, approximation, model, modeler, data, and population uncertainty. Presented at the International Conference of Clinical Biostatistics, 29th of August 2023, Milan, Italy.
Journal Club - Discussion of Heriot et al. Criteria for identifying patients ...Salpy Kelian
Discussing, using many Bobs, how a monte carlo simulation works for a Journal Club paper regarding the modality used for detection of infectious endocarditis.
A short introduction to sample size estimation for Research methodology workshop at Dr. BVP RMC, Pravara Institute of Medical Sciences(DU), Loni by Dr. Mandar Baviskar
Tom Selleck Health: A Comprehensive Look at the Iconic Actor’s Wellness Journeygreendigital
Tom Selleck, an enduring figure in Hollywood. has captivated audiences for decades with his rugged charm, iconic moustache. and memorable roles in television and film. From his breakout role as Thomas Magnum in Magnum P.I. to his current portrayal of Frank Reagan in Blue Bloods. Selleck's career has spanned over 50 years. But beyond his professional achievements. fans have often been curious about Tom Selleck Health. especially as he has aged in the public eye.
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Introduction
Many have been interested in Tom Selleck health. not only because of his enduring presence on screen but also because of the challenges. and lifestyle choices he has faced and made over the years. This article delves into the various aspects of Tom Selleck health. exploring his fitness regimen, diet, mental health. and the challenges he has encountered as he ages. We'll look at how he maintains his well-being. the health issues he has faced, and his approach to ageing .
Early Life and Career
Childhood and Athletic Beginnings
Tom Selleck was born on January 29, 1945, in Detroit, Michigan, and grew up in Sherman Oaks, California. From an early age, he was involved in sports, particularly basketball. which played a significant role in his physical development. His athletic pursuits continued into college. where he attended the University of Southern California (USC) on a basketball scholarship. This early involvement in sports laid a strong foundation for his physical health and disciplined lifestyle.
Transition to Acting
Selleck's transition from an athlete to an actor came with its physical demands. His first significant role in "Magnum P.I." required him to perform various stunts and maintain a fit appearance. This role, which he played from 1980 to 1988. necessitated a rigorous fitness routine to meet the show's demands. setting the stage for his long-term commitment to health and wellness.
Fitness Regimen
Workout Routine
Tom Selleck health and fitness regimen has evolved. adapting to his changing roles and age. During his "Magnum, P.I." days. Selleck's workouts were intense and focused on building and maintaining muscle mass. His routine included weightlifting, cardiovascular exercises. and specific training for the stunts he performed on the show.
Selleck adjusted his fitness routine as he aged to suit his body's needs. Today, his workouts focus on maintaining flexibility, strength, and cardiovascular health. He incorporates low-impact exercises such as swimming, walking, and light weightlifting. This balanced approach helps him stay fit without putting undue strain on his joints and muscles.
Importance of Flexibility and Mobility
In recent years, Selleck has emphasized the importance of flexibility and mobility in his fitness regimen. Understanding the natural decline in muscle mass and joint flexibility with age. he includes stretching and yoga in his routine. These practices help prevent injuries, improve posture, and maintain mobilit
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
Anti ulcer drugs and their Advance pharmacology ||
Anti-ulcer drugs are medications used to prevent and treat ulcers in the stomach and upper part of the small intestine (duodenal ulcers). These ulcers are often caused by an imbalance between stomach acid and the mucosal lining, which protects the stomach lining.
||Scope: Overview of various classes of anti-ulcer drugs, their mechanisms of action, indications, side effects, and clinical considerations.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
46. 50/50 Split in the Data Filter data to mortality outcome Filter data to non-mortality outcome Use PROC SURVEYSELECT to extract a subsample of non-mortality outcome Append the mortality outcome data to subsample
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