Impact of crossover and baseline prognostic factors on overall survival (OS) with abiraterone acetate (AA) in the COU-AA-302 final analysis.
Bibliographic record
Abstract
142 Background: AA + prednisone (P) significantly increased OS, time to opiate use, and was well tolerated at the COU-AA-302 final analysis. Here we further characterize OS benefit adjusting for crossover therapy and for baseline prognostic factors. Methods: Patients (N = 1,088) were randomized 1:1 to receive AA (1 g) + P (5 mg po BID) vs P. Co-primary end points were radiographic progression-free survival and OS. Median time to events with 95% CI was estimated using the Kaplan-Meier method. Stratified log-rank test was used to test the difference in treatment effect. Adjustment for crossover utilized the iterative parameter estimate (IPE) and impact of baseline prognostic factors was examined via the multivariate Cox proportional hazard model. Results: With a median follow-up of 49.2 months and 741 deaths (96% of required), AA + P significantly reduced the risk of death vs P (19%) and prolonged median OS (34.7 vs 30.3 months) (Table). 44% of patients initially receiving P alone subsequently received AA + P as crossover per protocol (17%) or as subsequent therapy (27%). IPE adjustment resulted in a 26% reduction in the risk of death (Table). By multivariate analysis, AA + P treatment led to a 21% reduction in the risk of death; baseline prostate-specific antigen (PSA), lactate dehydrogenase (LDH), hemoglobin, alkaline phosphatase (ALP), bone metastases, and age were significant OS prognostic factors (Table). Conclusions: AA + P yielded a statistically significant improvement in OS. Greater improvement in OS was observed after adjusting for the 44% of patients originally on P who ultimately received AA + P. Adjusting for baseline prognostic factors also demonstrated an AA + P OS benefit. Clinical trial information: NCT00887198. [Table: see text]
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".