Identification of subgroups of metastatic castrate-resistant prostate cancer (mCRPC) patients treated with abiraterone plus prednisone at low- vs. high-risk of radiographic progression: An analysis of COU-AA-302
Bibliographic record
Abstract
INTRODUCTION: Radiographic imaging is used to monitor disease progression for men with metastatic castrate-resistant prostate cancer (mCRPC). The optimal frequency of imaging, a costly and limited resource, is not known. Our objective was to identify predictors of radiographic progression to inform the frequency of imaging for men with mCRPC. METHODS: We accessed data for men with chemotherapy-naive mCRPC in the abiraterone acetate plus prednisone (AA-P) group of a randomized trial (COU-AA-302) (n=546). We used Cox proportional hazards modelling to identify predictors of time to progression. We divided patients into groups based on the most important predictors and estimated the probability of radiographic progression-free survival (RPFS) at six and 12 months. RESULTS: Baseline disease and change in prostate-specific antigen (PSA) at eight weeks were the strongest determinants of RPFS. The probability of RPFS for men with bone-only disease and a ≥50% fall in PSA was 93% (95% confidence interval [CI] 87-96) at six months and 80% (95% CI 72-86) at 12 months. In contrast, the probability of RPFS for men with bone and soft tissue metastasis and <50% fall in PSA was 55% (95% CI 41-67) at six months and 34% (95% CI 22-47) at 12 months. These findings should be externally validated. CONCLUSIONS: Patients with chemotherapy-naive mCRPC treated with first-line AA-P can be divided into groups with significantly different risks of radiographic progression based on a few clinically available variables, suggesting that imaging schedules could be individualized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".