Improvement in overall and cancer‐specific survival in contemporary, metastatic prostate cancer chemotherapy exposed patients
Bibliographic record
Abstract
INTRODUCTION: Over the last decade, multiple clinical trials demonstrated improved survival after chemotherapy for metastatic prostate cancer (mPCa). However, real-world data validating this effect within large-scale epidemiological data sets are scarce. We addressed this void. MATERIALS AND METHODS: Men with de novo mPCa were identified and systemic chemotherapy status was ascertained within the Surveillance, Epidemiology, and End Results database (2004-2016). Patients were divided between historical (2004-2013) versus contemporary (2014-2016). Chemotherapy rates were plotted over time. Kaplan-Meier plots and Cox regression models with additional multivariable adjustments addressed overall and cancer-specific mortality. All tests were repeated in propensity-matched analyses. RESULTS: Overall, 19,913 patients had de novo mPCa between 2004 and 2016. Of those, 1838 patients received chemotherapy. Of 1838 chemotherapy-exposed patients, 903 were historical, whereas 905 were contemporary. Chemotherapy rates increased from 5% to 25% over time. Median overall survival was not reached in contemporary patients versus was 24 months in historical patients (hazard ratio [HR]: 0.55, p < 0.001). After propensity score matching and additional multivariable adjustment (age, prostate-specific antigen, GGG, cT-stage, cN-stage, cM-stage, and local treatment) a HR of 0.55 (p < 0.001) was recorded. Analyses were repeated for cancer-specific mortality after adjustment for other cause mortality in competing risks regression models and recorded virtually the same findings before and after propensity score matching (HR: 0.55, p < 0.001). CONCLUSIONS: In mPCa patients, chemotherapy rates increased over time. A concomitant increase in survival was also recorded.
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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 | 0.000 |
| 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 | 0.000 |
| 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".