Applying Quality Indicators to Examine Quality of Care During Active Surveillance in Low-Risk Prostate Cancer: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Although a few studies have reported wide variations in quality of care in active surveillance (AS), there is a lack of research using validated quality indicators (QIs). The aim of this study was to apply evidence-based QIs to examine the quality of AS care at the population level. METHODS: QIs were measured using a population-based retrospective cohort of patients with low-risk prostate cancer diagnosed between 2002 and 2014. We developed 20 QIs through a modified Delphi approach with clinicians targeting the quality of AS care at the population level. QIs included structure (n=1), process of care (n=13), and outcome indicators (n=6). Abstracted pathology data were linked to cancer registry and administrative databases in Ontario, Canada. A total of 17 of 20 QIs could be applied based on available information in administrative databases. Variations in QI performance were explored according to patient age, year of diagnosis, and physician volume. RESULTS: The cohort included 33,454 men with low-risk prostate cancer, with a median age of 65 years (IQR, 59-71 years) and a median prostate-specific antigen level of 6.2 ng/mL. Compliance varied widely for 10 process QIs (range, 36.6%-100.0%, with 6 [60%] QIs >80%). Initial AS uptake was 36.6% and increased over time. Among outcome indicators, significant variations were observed by patient age group (10-year metastasis-free survival was 95.0% for age 65-74 years and 97.5% in age <55 years) and physician average annual AS volume (10-year metastasis-free survival was 94.5% for physicians with 1-2 patients with AS and 95.8% for those with ≥6 patients with AS annually). CONCLUSIONS: This study establishes a foundation for quality-of-care assessments and monitoring during AS implementation at a population level. Considerable variations appeared with QIs related to process of care by physician volume and Qis related to outcome by patient age group. These findings may represent areas for targeted quality improvement initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".