Using RIGHT (Reporting Items for Practice Guidelines in Healthcare) to evaluate the reporting quality of WHO guidelines
Bibliographic record
Abstract
BACKGROUND: Without adequate reporting of research, valuable time and resources are wasted. In the same vein, adequate reporting of practice guidelines to optimise patient care is equally important. Our study examines the quality of reporting of published WHO guidelines, over time, using the RIGHT (Reporting Items for Practice Guidelines in HealThcare) reporting checklist. METHODS: We examined English-language guidelines approved by the WHO Guidelines Review Committee from inception of the committee in 2007 until 31 December 2017. Pairs of independent, trained reviewers assessed the reporting quality of these guidelines. Descriptive data were summarised with frequencies and percentages. RESULTS: We included 182 eligible guidelines. Overall, 25 out of the 34 RIGHT items were reported in 75% or more of the WHO guidelines. The reporting rates improved over time. Further, 90% of the guidelines reported document type in the title. The identification of evidence, the rationale for recommendations and the review process were reported in more than 80% of guidelines. The certainty of the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system was assessed in 81% of the guidelines assessed. While 82% of guidelines reported funding sources, only 25% mentioned the role of funders. CONCLUSIONS: WHO guidelines provide adequate reporting of many of the RIGHT items and reporting has improved over time. WHO guidelines compare favourably to guidelines produced by other organisations. However, reporting can be further improved in a number of areas.
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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 | 0.652 | 0.855 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.032 | 0.028 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".