Is the AGREE II framework the appropriate tool to evaluate global urolithiasis guidelines?
Bibliographic record
Abstract
Zou and colleagues have penned an interesting manuscript in which they evaluated existing urolithiasis clinical practice guidelines (CPGs) using the Appraisal of Guidelines for Research & Evaluation (AGREE II) Instrument (1).AGREE II is a well-regarded, internationally recognized tool, primarily developed by a Canadian consortium in 2009, that assesses the quality of guideline development and reporting.It has been used in various urologic contexts, including the evaluation of guidelines for benign prostatic hyperplasia, erectile dysfunction, and urinary stomas, with at least 15 applications thus far.In this study, the authors identified 19 urolithiasis CPGs, written in either Chinese or English, through a systematic review spanning the past 13 years.These guidelines were then analyzed using AGREE II and only 5 guidelines were deemed "strongly recommended" due to their quality.It is worth noting that all Chinese guidelines were eventually excluded due to a lack of evidence-based recommendations.This raises concerns about the criteria used and potential Western biases in AGREE II that might lead to other non-Western guidelines failing in a similar way.Since the study only assessed guidelines in English or Chinese, we wonder if other non-Western guidelines would also fail and whether AGREE II is the best tool to assess all guidelines, regardless of origin.Furthermore, this study raises significant concerns about the number of subpar-
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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 | 0.143 | 0.599 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".