Quality of Care and Long-term Outcomes of Active Surveillance: Gaining Insights from Population-level Perspectives
Bibliographic record
Abstract
Active surveillance (AS) for low-risk prostate cancer (PC) is evolving as an internationally recognized standard of care and is recognized by major urological associations as a preferred treatment option. AS is increasingly used worldwide, yet research on variations of quality of care during AS is lacking, and compliance with AS guidelines is largely unknown at the population level. The aims of this research were to develop quality indicators (QIs) for AS, evaluate the quality of care for patients on AS using those QIs, and estimate long-term oncological outcomes after initial AS. A first study aimed to develop national consensus-based system-level QIs for evaluation of AS care among patients with low-risk PC. A second study aimed to critically apply QIs to examine/understand quality of care during AS at the population level. A third study aimed to evaluate long-term oncological outcomes at the population level in patients who received initial AS. This thesis establishes a foundation on which to measure and monitor the quality of care for AS patients at the population level. We developed 20 comprehensive structure-process-outcome-based QIs for measuring quality of AS care. Further, by applying these AS QIs to a large Canadian population database, we identified current gaps in the quality of AS care. The findings suggest that process of care QIs measured at PC diagnosis and during eligibility assessment for AS were improving in recent years; however, considerable variation appeared with many process indicators during the AS follow-up phase. Finally, our third study is one of the first to measure long-term oncological outcomes of AS using population-level data compared with initial treatment. Among men diagnosed with low-risk PC and managed with AS, the results suggest excellent 10- and 15-year metastasis-free survival, overall survival, and PC-specific mortality, similar to published observational studies from academic centres. However, at a population level, AS was associated with slightly worse long-term metastasis-free survival, overall survival and PC-specific mortality compared with initial treatment. These results should help clinicians and policy makers understand the impact of AS on long-term outcomes after widespread adoption.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".