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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.498 | 0.885 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.029 | 0.006 |
| Bibliometrics | 0.006 | 0.027 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.012 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".