Treatment intensification in metastatic castration-sensitive prostate cancer: a real-world study in Alberta, Canada
Bibliographic record
Abstract
AIM: To assess the current status of and factors associated with treatment intensification (TI) (with androgen receptor pathway inhibitors [ARPIs] and/or docetaxel) for metastatic castration-sensitive prostate cancer (mCSPC) in Canada. MATERIALS & METHODS: Retrospective analysis of data for 431 patients with mCSPC from the Alberta Prostate Cancer Research Initiative database (July 2014-March 2022). The primary objective was to assess the patient proportion receiving TI, time to TI, and associated factors. The secondary and exploratory objectives were evaluating TI patterns and factors associated with choice of therapy, respectively. RESULTS: Overall, 42% of patients received TI; most (65%) within 3 months post-index. TI was likely to occur within 3 months post-index in de novo mCSPC, but occurred later for recurrent mCSPC. Patients with recurrent mCSPC (HR [95% CI]: 0.52 [0.38-0.72]) and those aged ≥ 75 years (0.57 [0.36-0.93]) were less likely to receive TI. Patients with multiple metastatic sites and bone metastasis had a 2-3-fold higher likelihood of receiving TI. An ARPI was predominantly used (75%) for TI (median duration: 16.0 months). CONCLUSION: TI rates for mCSPC are suboptimal in Canada especially for older patients and those with recurrent mCSPC. TI prioritization in such groups may improve patient outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".