Methodological components and quality of evidence summaries: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to identify and map the available evidence related to evidence summary methodologies and indicators of quality. INTRODUCTION: It can be challenging for clinicians and policy makers to keep up-to-date with current evidence and best practice. An evidence summary is a way to provide health care decision makers with the most recent, highest quality evidence available on a particular topic in an easily digestible format to facilitate evidence-based clinical decisions. However, objectively evaluating the methodological quality of these types of evidence reviews is challenging. INCLUSION CRITERIA: Articles, papers, books, dissertations, reports and websites will be included if they evaluate, or describe the development or appraisal of, an evidence summary methodology. METHODS: A three-step search strategy will be used to find both published and unpublished literature. The following databases will be searched: US National Library of Medicine Database (PubMed) Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, ProQuest Dissertations and Theses, and Embase. The gray literature search will include relevant government and university websites, the Health Evidence Network website, the World Health Organization (WHO) Health Evidence Network website, the McMaster Health Systems Evidence website, and relevant websites included in the Canadian Agency for Drugs and Technologies in Health (CADTH) Grey Matters Handbook. Sources published in English will be considered, with no date limitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.386 | 0.826 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.041 | 0.008 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".