Cancer Drug Use in the Last Month of Life in Men With Castration-Resistant Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Several new drug therapies have been approved in CRPC in the past decade. However, little is known about their potential overuse at the end of life. Cancer therapy use at the end of life has been considered an indicator of overtreatment. The study objective was to describe CRPC drug use in the last month of life of CRPC patients in Quebec. PATIENTS AND METHODS: Using administrative databases from the province of Quebec in Canada, we identified patients who received medical or surgical castration treatment, received one or more CRPC drugs (chemotherapy, abiraterone, or bone-targeted therapy), and died between 2001 and 2013. CRPC drug use in the last month of life was the primary outcome. RESULTS: The cohort consisted of 1,148 patients with CRPC. A total of 316 men (27.5%) received a CRPC drug in the last month of life. For those who received chemotherapy, abiraterone, and bone-targeted therapy, 10.2%, 27.8%, and 31.8% received them in the last month of life, respectively. In multivariable analyses, age older than 75 years (odds ratio [OR], 0.75; 95% CI, 0.57 to 0.99), and prostate cancer diagnosis received less than 24 months earlier (OR, 0.43; 95% CI, 0.26 to 0.72) were associated with less CRPC drug use. Relative to dying between 2005 and 2011, dying between 2012 and 2013 (OR 1.60; 95% CI, 1.18 to 2.18) was associated with greater CRPC drug use. CONCLUSION: More than one quarter of patients received CRPC drug therapies in the last month of life. Persistent chemotherapy, abiraterone, bone-targeted therapies, and medical castration drugs in the last month of life may be an indicator of inappropriate and expensive end-of-life care.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".