Rates of primary and secondary treatments for patients on active surveillance for localized prostate cancer—A population‐based cohort study
Bibliographic record
Abstract
BACKGROUND: The rate of primary and secondary treatment while on active surveillance (AS) for localized prostate cancer at the general population level is unknown. Our objective was to determine the patterns of secondary treatments after primary surgery or radiation for patients who undergo AS. METHODS: This was a population-based retrospective cohort study of men aged 50-80 years old in Ontario, Canada, between 2008 and 2016. We identified 26 742 patients with prostate cancer, a Gleason grade score ≤7, and an index prostate-specific antigen ≤10 ng/mL. Patients were categorized as undergoing AS with or without delayed primary treatment (DT; treatment >6 months after diagnosis) versus immediate treatment (IT; treatment ≤6 months). Patients receiving DT and IT were propensity score matched and the rate of secondary treatment (surgery or radiation ± androgen deprivation treatment) was compared using Cox proportional hazards models. RESULTS: We identified 10 214 patients who underwent AS and 11 884 patients who underwent IT. Among patients undergoing AS, 3724 (36.5%) eventually underwent DT and among them, 406 (10.9%) underwent secondary treatment. The median time to DT was 1.2 years (IQR 0.5-8.1 years). The relative rate of undergoing secondary treatment was similar in the DT vs IT group (HR 0.92; 95% CI: 0.79-1.08). The risk of death in the DT group was higher compared to patients who did not undergo treatment (HR 1.23, 95% CI: 1.01-1.49). CONCLUSIONS: Among patients with localized prostate cancer on AS, one third undergo DT. The rate of secondary treatment was similar between the DT and IT groups. Patients in the DT group may experience a higher risk of mortality compared to those who remained 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".