Assessing the methodologic heterogeneity of Canadian Urological Association guidelines
Bibliographic record
Abstract
INTRODUCTION: The Canadian Urological Association (CUA) has a longstanding tradition of developing evidence-based guidelines. We conducted this study to assess the heterogeneity of the CUA's guideline methodology for developing recommendations from 2018-2023. METHODS: We included guidelines from the CUA website from 2018-2023. Two reviewers working independently and in duplicate abstracted all data points and categorized the reported methodologic approaches for formulating recommendations and rating the evidence. We performed descriptive statistics only. RESULTS: We included 23 guideline documents with a total of 654 recommendations. The median number of recommendations per guideline was 25 (interquartile range 17, 35). Seven guidelines (187 recommendations) used a modified Oxford Center for Evidence-Based Medicine approach for both the strength of recommendations and the levels of evidence, and eight guidelines (177 recommendations) reported the use of GRADE both for the strength of recommendations and the certainty of evidence. Of the remaining eight guidelines, four (154 recommendations) blended the GRADE approach for the strength of recommendations with modified Oxford levels of evidence, and the remaining four combined the American Urological Association's approach to recommendations with Oxford levels of evidence (n=1), GRADE certainty of evidence (n=2), or used GRADE but made no recommendations (n=1). CONCLUSIONS: CUA guidelines have been marked by considerable methodologic heterogeneity that may confuse end users. Continued advancement in the CUA's approach to guideline development will facilitate greater collaboration and resource sharing, thereby supporting the CUA's mission of promoting high-quality, evidence-based care.
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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 | 0.640 | 0.902 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.041 | 0.042 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".