National consensus quality indicators to assess quality of care for active surveillance in low-risk prostate cancer: An evidence-informed modified Delphi survey of Canadian urologists/radiation oncologists
Bibliographic record
Abstract
INTRODUCTION: Although many low-risk prostate cancer (PCa) patients worldwide currently receive active surveillance (AS), adherence to clinical guidelines on AS and variations in care at the population level remain poorly understood. We sought to develop system-level quality indicators (QIs) and performance measures for benchmarking the quality of care during AS. METHODS: Convenience sampling methods were used to identify an expert panel among practicing urologists and radiation oncologists across Canada. QI development involved two phases: 1) proposed QIs were identified through a literature search and published clinical guidelines on AS; and 2) indicators were selected through a modified Delphi process during which each panelist independently rated each indicator based on clinical importance. QI items were chosen as appropriate measures for quality of AS care if they met prespecified criteria (disagreement index <1 and median importance of ≥7 on a nine-point scale). RESULTS: Among 42 invited expert panel members, the response rate was 45% (n=19). Expert panel members were well-represented by type of physician (84% urologists, 16% radiation oncologists) and practice setting (79% academic, 21% non-academic). The expert panel endorsed 20 of 27 potential indicators as appropriate for measuring quality of AS care. CONCLUSIONS: We developed a set of QIs to measure AS care using published guidelines and clinical experts. Use of the indicators will be assessed for feasibility in healthcare databases. Reporting quality of care with these AS indicators may enhance adherence, reduce variation in care, and improve patient outcomes among low-risk PCa patients on AS.
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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 | 0.078 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".