Quality of care during active surveillance in low-risk prostate cancer patients: A population-based approach.
Bibliographic record
Abstract
16 Background: Active surveillance (AS) has become a widely accepted management strategy for low-risk (Gleason score ≤6) prostate cancer (PC). Given the large proportion of low-risk PC (60-90%) patients who currently receive AS, adherence to clinical guidelines on AS and variations in care at the population level remain surprisingly poorly understood. Further, it is presently unclear how often patients receive high quality AS care in community settings (almost all published data come from academic centers), yet the majority of AS occurs in community settings. Thus, there is significant interest in developing system-level quality indicators (QIs). We sought to develop structure-process-outcome-based QIs to enable benchmarking during AS follow-up using data available in Canadian administrative databases. Methods: We performed a detailed literature search on QIs in PC as well as broader theoretical concepts on QIs and consulted with clinical leaders from a major cancer centre. Current guidelines on AS and potential quality indicators were identified from a literature search. AS-specific QIs were tested among low-risk PC who were managed with AS between 2002-2011 using population-level cancer registry databases. We assessed adherence to clinical guidelines using QIs, and compared with health care system-related characteristics. Results: 25 indicators were proposed [structure of care (n = 5), process of care (n = 16) and health outcomes (n = 4)]. Overall 39% received AS, with 88% managed by a urologist. Only 43% of low volume (≤3 positive cores) patients underwent AS. Adherence of confirmatory biopsy with guidelines was performed on only 32% of patients, and adherence was better in higher volume institutions, among higher volume physicians, and in cancer centers. 5-and 10-year PC specific survival were significantly better among high volume physicians. Conclusions: We have proposed a set of QIs for measuring AS care. Initial data show that higher volume institution or higher volume physician and cancer center had better adherence to quality of AS care. Long term survival was better among patients treated by high volume physicians. Further validation of these QIs is ongoing.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".