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Tracking evidentiary claims in a systematic review: a case study of oral pain relief. Presented at AADR NIDCR Trainee Workshop 2016-03-16

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Jodi Schneider and Tanja Bekhuis. “Tracking evidentiary claims in a systematic review: a proposed case study of oral pain relief”. National Institute of Dental and Craniofacial Research Trainee Research Presentations, co-located with the American Association for Dental Research annual conference. Los Angeles, California, March 16, 2016.

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Tracking evidentiary claims in a systematic review: a case study of oral pain relief. Presented at AADR NIDCR Trainee Workshop 2016-03-16

  1. 1. University  of  Pi.sburgh   Department  of  Biomedical  Informa<cs   Tracking  eviden<ary  claims  in  a  systema<c   review:  a  proposed  case  study  of  oral  pain  relief   Jodi Schneider1,2, Tanja Bekhuis1,2 1Department of Biomedical Informatics, School of Medicine, University of Pittsburgh 2Center for Informatics in Oral Health Translational Research, School of Dental Medicine, University of Pittsburgh Problem   Evidence-based dentistry depends on systematic reviews to summarize the best available evidence in scientific reports. Producing systematic reviews is time-consuming and resource-intensive. Current best practice is for two or more people to independently extract data (Fig. 1). Semi-automation can reduce the labor associated with data extraction (Fig. 2).   An<cipated  Results   An annotated corpus for future research. We will use software such as ATLAS.ti to annotate and analyze textual data. Identification of zones in primary reports relevant for information extraction of each data type. A publishable case study.   Funded  by  training  grant  5T15LM007059-­‐29  from  the  U.S.  Na<onal  Library  of  Medicine   &  Na<onal  Ins<tute  of  Dental  and  Craniofacial  Research   Future  Work   The annotated corpus will be used to develop a semi-automated system to reduce the labor associated with extracting data. Additionally, we will conduct a contextual inquiry to better understand the practice of systematic reviewing. Relevant stakeholders include the ADA Center for Evidence-Based Dentistry and the Cochrane Oral Health group, among others.         Aim   Identify information in primary research reports used to summarize evidence in a systematic review. The long-term goal is to develop semi-automated support for data extraction in systematic reviewing. Design   Case study: In-depth analysis of a single systematic review on oral pain relief and its included studies: Bailey E, Worthington H, Coulthard P. Ibuprofen and/or paracetamol (acetaminophen) for pain relief after surgical removal of lower wisdom teeth, a Cochrane systematic review. Br Dent J. 2014;216(8):451-5. The reviewers presented their extracted data in a Characteristics of Included Studies table (see excerpt below). Six primary research reports were included. Using this table, we will annotate primary research articles to determine: •  Which data types and data are extracted from the primary articles? •  Where in the primary articles are types of data found? •  Are data copied directly from the primary article or paraphrased? Included  Reports  in  a   Systema4c  Review   Reviewer  1   Methods   ********   Par4cipants   _________   Interven4ons   _...   …   Extract  Data   Reviewer  2   Methods     Par4cipants     Interven4ons     …   Extract  Data   Discuss  &  Reconcile  Disagreements   Methods     Par4cipants     Interven4ons     …   Reviewer  1   Reviewer  2   Consensus   Fig.  1.  CURRENT  BEST  PRACTICE  At  least  2  reviewers  independently  extract  data  from  the  included  research  reports.     They  reconcile  differences  to  reach  consensus  before  synthesizing  the  evidence.     Included  Reports  in  a     Systema4c  Review   Reviewer   Methods       Par4cipants       Interven4ons       …   Extract  Data   Computer  Assistance   Methods       Par4cipants       Interven4ons       …   Extract  Data   Display  &  Reconcile  Disagreements   Methods       Par4cipants     Interven4ons       …   Reviewer   Automated   System   Consensus   Fig.  2.  PROPOSED  SEMI-­‐AUTOMATION  A  semi-­‐automated  system  can  support  a  single  reviewer  during  data  extrac4on.     Differences  in  the  informa4on  extracted  by  a  human  reviewer  and  a  computerized  system  are  displayed.  The  reviewer  decides   on  the  consensus  version.  

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