A <scp>population‐based</scp> study of factors associated with systemic treatment in advanced prostate cancer decedents
Bibliographic record
Abstract
INTRODUCTION: Life-prolonging therapies (LPTs) are rapidly evolving for the treatment of advanced prostate cancer, although factors associated with real-world uptake are not well characterized. METHODS: In this cohort of prostate-cancer decedents, we analyzed factors associated with LPT access. Population-level databases from Ontario, Canada identified patients 65 years or older with prostate cancer receiving androgen deprivation therapy and who died of prostate cancer between 2013 and 2017. Univariate and multivariable analyses assessed the association between baseline characteristics and receipt of LPT in the 2 years prior to death. RESULTS: Of 3575 patients who died of prostate cancer, 40.4% (n = 1443) received LPT, which comprised abiraterone (66.3%), docetaxel (50.3%), enzalutamide (17.2%), radium-223 (10.0%), and/or cabazitaxel (3.5%). Use of LPT increased by year of death (2013: 22.7%, 2014: 31.8%, 2015: 41.8%, 2016: 49.1%, and 2017: 57.9%, p < 0.0001), driven by uptake of all agents except docetaxel. Adjusted odds of use were higher for patients seen at Regional Cancer Centers (OR: 1.8, 95% CI: 1.5-2.1) and who received prior prostate-directed therapy (OR: 1.3, 95% CI: 1.0-1.5), but lower with advanced age (≥85: OR: 0.54, 95% CI:0.39-0.75), increased chronic conditions (≥6: OR: 0.62, 95% CI: 0.43-0.92), and long-term care residency (OR: 0.38, 95% CI: 0.17-0.89). Income, stage at presentation, and distance to the cancer center were not associated with LPT uptake. CONCLUSION: In this cohort of prostate cancer-decedents, real-world uptake of novel prostate cancer therapies occurred at substantially higher rates for patients receiving care at Regional Cancer Centers, reinforcing the potential benefits for treatment access for patients referred to specialist centers.
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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 | 0.003 |
| 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) | 0.002 | 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".