External Validation of the International Staging Collaboration for Cancer of the Prostate (STAR-CAP) Prognostic System in Patients Managed with Active Surveillance
Bibliographic record
Abstract
STAR-CAP is a system for assigning clinical prognostic groups in prostate cancer (PC). The system was developed and validated in patients with PC managed with radical treatment (rTx) with curative intent and outperforms existing systems for risk stratification. We evaluated STAR-CAP in patients managed with active surveillance (AS) in two independent prospective cohorts (Princess Margaret Cancer Centre [PMH] and Sunnybrook Odette Cancer Centre [OCC]) between January 1, 1995, and August 1, 2021. Baseline data were evaluated and patients were categorised into nine STAR-CAP stage groups (IA-IIIC) using a point system for six variables: age, prostate-specific antigen (PSA), cT stage, cN stage, Gleason grade, and percentage positive cores. PC-specific mortality (PCSM) and progression to rTx were quantified across the STAR-CAP groups. Of the 4099 men included, 86% had cT1a-c disease and 89% had grade group 1 PC. PSA at diagnosis ranged from 4.0 to 7.9 ng/ml. Median follow-up was 5.7 yr at PMH and 10.8 yr at OCC. The proportion of patients who went on to receive rTx was 30% at PMH and 43% at OCC. The estimated 10-yr and 15-yr PCSM incidence rates were 0.4% (95% confidence interval [CI] 0.1-1.0%) and 1.1% (95% CI 0.3-2.9%) at PMH, and 1.35% (95% CI 0.79-2.18%) and 3.15% (95% CI 2.14-4.47%) at OCC, respectively. Calibration analysis showed close agreement between observed and STAR-CAP-predicted PCSM. Our external validation supports use of STAR-CAP as a singular classification system and nomenclature for more consistent decision-making across the PC continuum. Patient summary: We looked at how well a tool called the STAR-CAP system predicts outcomes for men diagnosed with prostate cancer who are on active surveillance instead of undergoing immediate treatment. After 10 years and 15 years, only a small number of men in the lowest STAR-CAP categories had died from prostate cancer. STAR-CAP can help doctors and patients make better informed decisions on how to manage their prostate cancer.
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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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".