Using real-world, population-level data to assess the uptake of active surveillance for low-grade prostate cancer before and after the release of clinical guidelines
Bibliographic record
Abstract
INTRODUCTION: Clinical guidelines recommend active surveillance (AS) as the preferred strategy for men with localized grade group (GG) 1 prostate cancer (PCa). We determined if the percentage of GG1 PCa patients in Ontario, Canada, managed by AS changed after the introduction of AS clinical guidelines and assessed adherence to the recommended followup protocol. METHODS: Using Ontario administrative databases, we conducted a time series analysis (autoregressive integrated moving average [ARIMA] models) in a population-based cohort of men diagnosed with GG1 PCa (2010-2018). Men were classified as managed by AS if they had repeat (confirmatory) biopsy within two years. Sensitivity analyses (treatment classification variation) and secondary analyses (low-risk GG1 and GG2 PCa) were conducted. RESULTS: We identified 12 236 eligible GG1 PCa patients, of which 7749 (63.3%) were initially managed by AS. Percentage AS increased from 44% in 2010 to 81% in 2018. Interrupted time series analysis estimated an immediate step change of 6.2 percentage points (95% confidence interval [CI] 3.0, 9.4) and a difference in slope of -2.3 percentage points (95% CI -6.9, 2.3) per year. Findings were robust to sensitivity analyses and similar for low-risk PCa. Adherence to monitoring and AS uptake in GG2 patients were not associated with guideline publication. Limitations include lack of treatment intent information in administrative data. CONCLUSIONS: The use of AS for low-grade PCa patients in Ontario increased from almost one in two patients in 2010 to four in five patients in 2017/2018. Adoption appeared to reflect the growing acceptance of AS prior to the guidelines, as well as an increase in response to the guideline introduction.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".