Assessing the Quality of Global Clinical Practice Guidelines on Gout Using AGREE II Instrument
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to assess the quality of global clinical practice guidelines (CPGs) on gout. METHODS: We systematically searched MEDLINE, CBM (Chinese Biomedical Literature database), GIN (Guidelines International Network), NICE (National Institute for Health and Clinical Excellence), NGC (National Guideline Clearinghouse), WHO (World Health Organization), SIGN (Scottish Intercollegiate Guidelines Network), DynaMed, UpToDate, and Best Practice databases from their inception until January 2017 to identify and select CPGs related to gout. Two reviewers independently assessed the eligible gout CPGs using the AGREE II instrument. RESULTS: We evaluated 15 CPGs published between 2007 and 2017, produced by 13 different developers. Quality of evidence and strength of recommendations were presented in 14 (93%) and 13 (87%) CPGs, respectively. The mean scores (±SD) for each AGREE II domain were as follows: (i) scope and purpose: 75% (±17%), (ii) stakeholder involvement: 39% (±19%), (iii) rigor of development: 43% (±17%), (iv) clarity and presentation: 82% (±14%), (v) applicability: 31% (±12%), and (vi) editorial independence: 23% (±29%). CONCLUSIONS: The quality of gout CPGs was suboptimal, and various incompatible grading systems of quality of evidence and strength of recommendations were used. The use of a standardized international grading system is essential to ensure high methodological quality of gout CPGs. Tools such as AGREE II could substantially improve the development and update of future gout CPGs.
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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 | 0.230 | 0.402 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.028 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".