Structured evidence summaries designed to inform decision-makers in health research: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to identify, summarize, and describe the content and format of structured evidence summaries designed to inform clinical or policy decisions. INTRODUCTION: There is a need to develop a more efficient strategy to ensure that the results of the Living Evidence approach reach end users in a timely manner, thereby enhancing their role in the decision-making process. ELIGIBILITY CRITERIA: Any article assessing the development or validation process of generating a structured evidence summary aimed at informing health decision-makers will be considered for inclusion in the review. Additionally, we will include summaries that have been published as part of the updated reports of living systematic reviews of any health-related question. METHODS: This scoping review will be conducted in accordance with the JBI guidance for scoping reviews and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR). The initial search will be conducted in PubMed, Embase, and Cochrane, as well as websites and databases specializing in health decision-making and health technology assessment, including Health Systems Evidence, Epistemonikos, NICE Evidence Search, and websites of major European health technology assessment agencies, such as the European Network for Health Technology Assessment (EUnetHTA) and the National Institute for Health and Care Excellence (NICE). Finally, broader searches will be conducted in Google Scholar and the JBI Evidence-Based Practice Database to identify hard-to-find articles. Two researchers will independently screen, select, and extract documents, with findings presented both narratively and in tabular format. REVIEW REGISTRATION: OSF https://osf.io/69chn.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.229 | 0.220 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.102 | 0.041 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".