Common issues raised during the quality assurance process of WHO guidelines: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: In 2007, WHO established the Guidelines Review Committee (GRC) to ensure that WHO guidelines adhere to the highest international standards. The GRC reviews guideline proposals and final guidelines. The objectives of this study were to examine the rates of and reasons for conditional approval and non-approval of documents submitted for the first time to the GRC, and calculate the time intervals and numbers of submissions to achieve approval for documents conditionally approved or not approved at first submission. METHODS: All initial submissions to the GRC between 2014 and 2017 were examined. Data were extracted from the GRC's records of written comments and discussions. RESULTS: Of a total of 85 proposals and 88 final guidelines, 32 (37.6%) proposals and 37 (42.0%) final guidelines were conditionally approved, and 15 (17.6%) proposals and 28 (31.8%) final guidelines were not. For both conditionally approved and not approved proposals, the most frequent reasons were suboptimal composition or inadequate description of the guideline contributor groups (in all proposals), followed by inadequate formulation of key questions (in 90.6% of conditionally approved proposals and all not approved proposals). For both conditionally approved and not approved final guidelines, the most frequent reasons were problems with recommendations (in all final guidelines), followed by inappropriate methods for evidence retrieval or an inadequate description thereof (in all conditionally approved final guidelines and 75.0% of not approved final guidelines). The median time to achieve approval was 2 months for proposals and 1-2 months for final guidelines. The median number of submissions was 2 for proposals and 2-2.5 for final guidelines. CONCLUSION: The GRC implements a rigorous quality assurance process and identifies problems with a significant percentage of initial submissions. WHO needs to continuously evaluate its guideline development processes to inform effective quality improvement measures and optimise the quality of its guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".