Methods for living guidelines: early guidance based on practical experience. Paper 3: selecting and prioritizing questions for living guidelines
Bibliographic record
Abstract
OBJECTIVES: This article is part of a series on methods for living guidelines, consolidating practical experiences from developing living guidelines. It focuses on methods for identification, selection, and prioritization of clinical questions for a living approach to guideline development. STUDY DESIGN AND SETTING: Members of the Australian Living Evidence Consortium, the National Institute of Health and Care Excellence and the US Grading of Recommendations, Assessment, Development and Evaluations Network, convened a working group. All members have expertize and practical experience in the development of living guidelines. We collated methods, documents on prioritization from each organization's living guidelines, conducted interviews and held working group discussions. We consolidated these to form best practice principles which were then edited and agreed on by the working group members. RESULTS: We developed best practice principles for (1) identification, (2) selection, and (3) prioritization, of questions for a living approach to guideline development. Several different strategies for undertaking prioritizing questions are explored. CONCLUSION: The article provides guidance for prioritizing questions in living guidelines. Subsequent articles in this series explore consumer involvement, search decisions, and methods decisions that are appropriate for questions with different priority levels.
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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 | 0.137 | 0.945 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".