Model Risk Scores May Underestimate Rate of Biochemical Recurrence in African American Men with Localized Prostate Cancer: A Cohort Analysis of Over 3000 Men
Bibliographic record
Abstract
Abstract Introduction: This study aims to determine if there is a difference in the CAPRA and Kattan model-adjusted risk of biochemical recurrence (BCR) and/or adverse pathology between African American (AAM) and Caucasian men (CM) undergoing radical prostatectomy (RP). Methods: We identified men in the Pennsylvania Urologic Regional Collaborative (PURC) who underwent radical prostatectomy (RP). Cox proportional hazards regression models were used to compare the rate of BCR after RP between CM and AAM adjusting for the CAPRA, CAPRA-S, and pre- and post-operative Kattan model score. Logistic regression models were used to compare the rate of adverse pathology after RP between CM and AAM, adjusting for the same models. Results: The 2-year BCR free survival was lower in AAM (72.5%) compared to Caucasian men (CM) (79.0%), with a hazard ratio (HR) of 1.38 (95% CI 1.16-1.63, p<0.001). The rate of BCR was significantly greater in AAM compared to CM after adjustment for pre-op Kattan (HR 1.29; 95% CI 1.08-1.53; p=0.004), and post-op Kattan scores (HR 1.26; 95% CI 1.05-1.49; p<0.001). There was a trend towards higher BCR rates among AAM after adjustment for CAPRA (HR 1.13; 95% CI 0.95-1.35; p=0.17) and CAPRA-S (HR 1.11; 95% 0.93-1.32; p=0.25), which did not reach statistical significance. The overall rate of adverse pathology was similar between AAM (38.4%) and CM (37.8%) (OR 1.02; 95% CI 0.89-1.17; p=0.72) but was significantly greater in AAM compared to CM after adjusting for CAPRA (OR 1.28; 95% CI 1.10-1.50; p=0.001) and Kattan scores (OR 1.23; 95% CI 1.06-1.43; p=0.007). Conclusion: Our analysis from a large multicenter real world cohort provides further evidence that African American men may have a greater-than predicted rate of BCR and adverse pathology after RP than is currently predicted by CAPRA and Kattan models. Accordingly, AAM may benefit from more frequent use of adjuvant therapies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".