Active Treatment in Low-Risk Prostate Cancer: A Population-Based Study
Bibliographic record
Abstract
Background: Active surveillance instead of active treatment (at) is preferred for patients with low-risk prostate cancer (lr-pca), but practice varies widely. We conducted a population-based study to assess the proportion of patients who underwent at between January 2011 and December 2014, and to evaluate factors associated with at. Methods: The provincial cancer registry was linked to administrative health datasets to identify patients with lr-pca and to acquire demographic, tumour, and treatment data. The primary outcome was receipt of at during the first 12 months after diagnosis, defined as any receipt of external-beam radiotherapy, brachytherapy, radical prostatectomy, cryotherapy, or androgen deprivation. Univariate and multivariate logistic regression were used to analyze the correlation between patient and tumour factors and at. Results: Of 1565 patients with lr-pca, 554 (35.4%) underwent at within 12 months of diagnosis. Radical prostatectomy was the most common treatment (58%), followed by brachytherapy (29.6%). Younger age [odds ratio (or) 0.92; 95% confidence interval (ci): 0.91 to 0.94], lower score (≥3) on the Charlson comorbidity index (OR: 0.36; 95% ci: 0.19 to 0.68), T2 stage (or: 3.05; 95% ci: 2.03 to 4.58), higher prostate-specific antigen (psa) at diagnosis (or: 1.13; 95% ci: 1.06 to 1.21), radiation oncologist consultation (or: 3.35; 95% ci: 2.55 to 4.39), and earlier diagnosis year (2012 or: 0.46; 95% ci: 0.34 to 0.63; 2013 or: 0.45; 95% ci: 0.32 to 0.63; 2014 or: 0.33; 95% ci: 0.23 to 0.47) were associated with a higher probability of at. Conclusions: This contemporary population-based study demonstrates that approximately one third of patients with lr-pca undergo at. Patients of younger age, with less comorbidity, a higher tumour stage, higher psa, earlier year of diagnosis, and radiation oncologist consultation were more likely to undergo at. Further investigation is needed to identify strategies that could minimize overtreatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.002 | 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".