A critical appraisal of urolithiasis clinical practice guidelines using the AGREE II instrument
Bibliographic record
Abstract
Background: The Appraisal of Guidelines for Research and Evaluation II (AGREE II) instrument was developed to improve the methodological quality of clinical practice guidelines (CPGs). High-quality guidelines can provide reliable recommendations for different clinical issues. Currently, there is no quality appraisal of CPGs for urolithiasis. This study evaluated the quality of evidence-based CPGs for urolithiasis and provided new insights into improving guideline quality on urolithiasis. Methods: Systematic reviews were conducted to identify urolithiasis CPGs in PubMed, electronic databases, and websites of medical associations from January 2009 to July 2022. The quality of included CPGs was evaluated by four reviewers using the AGREE II instrument. Subsequently, the scores of all domains in the AGREE II instrument were calculated. Results: A total of 19 urolithiasis CPGs were identified for review: seven from Europe, six from USA, three from international union, two from Canada, and one from Asia. The agreement among reviewers was rated good [intraclass correlation coefficient (ICC), 0.806; 95% CI: 0.779-0.831]. The domains with the highest scores were scope and purpose (69.7%, 54.2-86.1%) and clarity of presentation (76.8%, 59.7-90.3%). The domains of stakeholder involvement (44.9%, 19.4-84.7%) and applicability (48.5%, 30.2-72.9%) gained the lowest score. Only five guidelines (26.3%) were considered "strongly recommended". Conclusions: The overall quality of the eligible CPGs was relatively high; however, future work is still needed in the domains of rigor of development, editorial independence, applicability, and stakeholder involvement.
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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 | 0.559 | 0.780 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.046 | 0.027 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".