Predictors of clinical outcomes in metastatic castration-resistant prostate cancer (mCRPC) patients treated with abiraterone acetate or placebo: An exploratory post-hoc analysis of COU-AA-302 trial data.
Bibliographic record
Abstract
e16529 Background: A post-hoc analysis of COU-AA-302 trial data showed that brief pain inventory (BPI), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), and bone metastases were predictors for overall survival in men with mCRPC treated with AA+P. This study aimed to identify baseline predictors of other clinical outcomes. Methods: COU-AA-302 trial data were used to develop predictive models for prostate-specific antigen (PSA) progression, Eastern Cooperative Oncology Group performance status (ECOG PS) deterioration, and opiate use. Associations between baseline factors and outcomes were first assessed using multivariable Cox models among AA+P patients (Step 1). In Step 2, interaction testing was conducted between treatment (AA+P vs. placebo) and each potential predictor (P < 0.2) identified in Step 1. Final Cox models included predictors and any significant interactions (p < 0.05). Results: A total of 1,034 men (525 AA+P; 509 placebo) were included in the analysis. Baseline BPI, PSA, LDH, and ALP were predictors of PSA progression and opiate use regardless of AA+P or placebo with higher values indicating higher risks (all P < 0.05). Younger age, shorter time from luteinizing hormone-releasing hormone to randomization, higher Gleason score, and lower PSA at diagnosis were also associated with higher risks of opiate use: (all P < 0.05). For ECOG PS deterioration, higher baseline BPI, PSA, LDH, and Gleason score, older age, and lower baseline ECOG were associated with higher risks regardless of AA+P or placebo (all P < 0.05). Baseline ALP and site of metastasis also predicted ECOG PS deterioration but the effect varied by treatment (lower risk in AA+P versus placebo; both interactions’ P < 0.05). Conclusions: Predictors of PSA progression, ECOG PS deterioration, and opiate use were identified in AA+P and placebo-treated men with mCRPC. No predictors were associated with worse outcomes for AA+P versus placebo, while the negative impact of certain predictors on ECOG PS was favorably modified by AA+P. Further study is needed on the relationship between AA+P and prognostic factors.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".