CAPRA-S predicts outcome for adjuvant and salvage EBRT after radical prostatectomy
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate the predictive value of the Cancer of the Prostate Risk Assessment Postsurgical Score (CAPRA-S) for patients treated with radical prostatectomy followed by subsequent external beam radiotherapy (EBRT). METHODS: A total of 373 patients treated with EBRT between January 2000 and June 2015 were identified in the institutional database. Followup and complete CAPRA-S score were available for 334 (89.5%) patients. CAPRA-S scores were sorted into previously defined categories of low- (score 0-2), intermediate- (3-5), and high-risk (6-12). Time to biochemical recurrence (BCR) was defined as prostate-specific antigen (PSA) >0.20 ng/mL after EBRT. Survival analyses were performed using the Kaplan-Meier method and comparisons were made using the log-rank test. RESULTS: Overall median time from surgery to EBRT was 18 months (interquartile range [IQR] 8-36) and median followup since EBRT was 48 months (IQR 28-78). CAPRA-S predicted time to BCR (<0.001), time to palliative androgen-deprivation therapy (ADT) (p=0.017), and a trend for significantly predicting overall survival (OS, p=0.058). On multivariate analysis, the CAPRA-S was predictive of time to BCR only (low-risk vs. intermediate-risk; hazard ratio [HR] 0.14, 95% confidence interval [CI] 0.043-0.48, p=0.001). The last PSA measurement before EBRT as a continuous and grouped variable proved highly significant in predicting all outcomes tested, including OS (p≤0.002). CONCLUSIONS: CAPRA-S predicts time to BCR and freedom from palliative ADT, and is borderline significant for OS. Together with the PSA before EBRT, CAPRA-S is a useful, predictive tool. The main limitation of this study is its retrospective design.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".