Impact of the introduction of novel hormonal agents on metastatic castration-resistant prostate cancer treatment choice
Bibliographic record
Abstract
BACKGROUND: Docetaxel-based chemotherapy has been the cornerstone of the management of symptomatic metastatic castration-resistant prostate cancer (mCRPC) since 2004. This study aimed to describe how real-world clinical practice was changed with the public funding of novel hormonal agents (abiraterone and enzalutamide) in Quebec. METHODS: We conducted a retrospective cohort study in two McGill University hospitals. Hospital-based cancer registries were used to select mCRPC patients in medical oncology departments from January 2010 to June 2014. Two groups according to mCRPC diagnosis year were built, with 2012 chosen as the cut-off year, corresponding to the year abiraterone was approved for public reimbursement in second-line in Quebec. Kaplan-Meier analysis was used to estimate time to first docetaxel prescription since mCRPC diagnosis before and after 2012. Cox regression was used to identify predictive factors of docetaxel and novel hormonal agent use. RESULTS: In our cohort, 308 patients diagnosed with mCRPC were selected with 162 patients in the pre-2012 group and 146 patients in the post-2012 group. The median age at mCRPC was 74.0 years old. At 12 months from diagnosis, 69% of patients received a prescription for docetaxel in the pre-2012 group comparatively to 53% in the post-2012 group. Factors that decreased the likelihood of docetaxel utilization were: age older than 80 at mCRPC diagnosis (HR: 0.5; 95%CI: 0.3-0.7), mCRPC diagnosis after 2012 (HR: 0.6; 95%CI: 0.4-0.8), and asymptomatic disease at mCRPC diagnosis (HR: 0.5; 95%CI: 0.3-0.7). CONCLUSION: The introduction of novel hormonal agents reduced first-line and overall docetaxel utilization and delayed time to its initiation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".