Development of rapid guidelines: 3. GIN-McMaster Guideline Development Checklist extension for rapid recommendations
Bibliographic record
Abstract
BACKGROUND: Practice guidelines require a substantial investment of resources and time, often taking between 1 and 3 years from conceptualisation to publication. However, urgent situations require the development of recommendations in a shorter timeframe. In this third and final article in the series exploring challenges and solutions in developing rapid guidelines (RGs), we propose guiding principles for the development of RGs. METHODS: We utilised the Guideline International Network-McMaster Guideline Development Checklist (GDC) as a starting point for elements to consider during RG development. We built on those elements using the findings from a systematic review of guideline manuals, a survey of international organisations conducting RGs, and interviews of guideline developers within WHO. We reviewed initial findings and developed an intermediate list of elements, as well as narrative guidance. We then invited experts to validate the intermediate list, reviewing for placement, brevity and redundancy. We used this iterative process and group consensus to determine the final elements for RG development guidance. RESULTS: Our work identified 21 principles within the topics of the Guideline International Network-McMaster GDC to guide the planning and development of RGs. Principles fell within 15 of the 18 checklist topics, highlighting strategies to streamline and expedite the guideline development process. CONCLUSIONS: We defined principles to guide the development of RGs, while maintaining a standardised, rigorous and transparent process. These principles will serve as guidance for guideline developers responding to urgent situations such as public health urgencies. Integration of these principles within currently disseminated guideline development standards will facilitate the use of those tools in situations necessitating RG recommendations.
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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 | 0.117 | 0.305 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.028 | 0.017 |
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".