Developing WHO rapid advice guidelines in the setting of a public health emergency
Bibliographic record
Abstract
OBJECTIVES: We describe newly established guidance for guideline developers at the World Health Organization (WHO) on the process and procedures for developing a rapid advice guideline in the context of a public health emergency (e.g., the 2014 Ebola epidemic). STUDY DESIGN AND SETTING: We based our approach on established rapid review methods, which were incorporated into existing WHO guideline development processes. Guidance was further informed by in-depth discussions of issues related to rapid guideline development with WHO staff (n = 6), who oversee the Organization's response to emergencies. RESULTS: We discuss criteria for considering if a rapid advice guideline is appropriate and feasible and outline the roles of various contributors across the phases of development. Further, we describe the methods and steps involved in performing rapid reviews, which are more fluid and iterative than for a standard guideline process. In general, rapid advice guidelines involve a shorter timeline, narrower scope, and the use of abbreviated methods for the evidence review. CONCLUSION: Important differences exist between developing a standard guideline and a rapid advice guideline. However, the core principles for WHO guidelines apply to rapid advice guidelines including minimizing bias, applying transparent processes and the use of explicit methods.
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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 | 0.032 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".