Methods for living guidelines: early guidance based on practical experience. Paper 5: decisions on methods for evidence synthesis and recommendation development for living guidelines
Bibliographic record
Abstract
OBJECTIVES: Producing living guidelines requires making important decisions about methods for evidence identification, appraisal, and integration to allow the living mode to function. Clarifying what these decisions are and the trade-offs between options is necessary. This article provides living guideline developers with a framework to enable them to choose the most suitable model for their living guideline topic, question, or context. STUDY DESIGN AND SETTING: We developed this guidance through an iterative process informed by interviews, feedback, and a consensus process with an international group of living guideline developers. RESULTS: Several key decisions need to be made both before commencing and throughout the continual process of living guideline development and maintenance. These include deciding what approach is taken to the systematic review process; decisions about methods to be applied for the evidence appraisal process, including the use of unpublished data; and selection of "triggers" to incorporate new studies into living guideline recommendations. In each case, there are multiple options and trade-offs. CONCLUSION: We identify trade-offs and important decisions to be considered throughout the living guideline development process. The most appropriate, and most sustainable, mode of development and updating will be dependent on the choices made in each of these areas.
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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 | 0.412 | 0.633 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.053 | 0.036 |
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; the direct Gemma label and the distilled Codex classifier 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".