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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".