Methods for living guidelines: early guidance based on practical experience. Paper 1: Introduction
Bibliographic record
Abstract
OBJECTIVES: To introduce methods for living guidelines based on practical experiences by the Australian Living Evidence Consortium (ALEC), the National Institute of Health and Care Excellence (NICE), and the Infectious Diseases Society of America (IDSA), with methodological support from the US Grading of Recommendations, Assessment, Development and Evaluations (GRADE) Network. STUDY DESIGN AND SETTING: Members of ALEC, NICE, and the US GRADE Network, convened a working group to share experiences of the methods used to develop living guidelines and outline the key differences between traditional and living guidelines methods. RESULTS: The guidance includes the following steps: 1) deciding if the guideline is a priority for a living approach, 2) preparing for living guideline development, 3) literature surveillance and frequency of searching, 4) assessment and synthesis of the evidence, 5) publication and dissemination, and 6) transitioning recommendations out of living mode. CONCLUSION: This paper introduces methods for living guidelines and provides examples of the similarities and differences in approach across multiple organizations conducting living guidelines. It also introduces a series of papers exploring methods for living guidelines based on our practical experiences, including consumer involvement, selecting and prioritizing questions, search decisions, and methods decisions.
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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 | 0.217 | 0.396 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.021 |
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".