Prostate cancer risk prediction using the novel versions of the European Randomised Study for Screening of Prostate Cancer ( <scp>ERSPC</scp> ) and Prostate Cancer Prevention Trial ( <scp>PCPT</scp> ) risk calculators: independent validation and comparison in a contemporary European cohort
Bibliographic record
Abstract
OBJECTIVES: To externally validate and compare the two novel versions of the European Randomised Study for Screening of Prostate Cancer (ERSPC)-prostate cancer risk calculator (RC) and Prostate Cancer Prevention Trial (PCPT)-RC. PATIENTS AND METHODS: All men who underwent a transrectal prostate biopsy in a European tertiary care centre between 2004 and 2012 were retrospectively identified. The probability of detecting prostate cancer and significant cancer (Gleason score ≥7) was calculated for each man using the novel versions of the ERSPC-RC (DRE-based version 3/4) and the PCPT-RC (version 2.0) and compared with biopsy results. Calibration and discrimination were assessed using the calibration slope method and the area under the receiver operating characteristic curve (AUC), respectively. Additionally, decision curve analyses were performed. RESULTS: Of 1 996 men, 483 (24%) were diagnosed with prostate cancer and 226 (11%) with significant prostate cancer. Calibration of the two RCs was comparable, although the PCPT-RC was slightly superior in the higher risk prediction range for any and significant prostate cancer. Discrimination of the ERSPC- and PCPT-RC was comparable for any prostate cancer (AUCs 0.65 vs 0.66), while the ERSPC-RC was somewhat better for significant prostate cancer (AUCs 0.73 vs 0.70). Decision curve analyses revealed a comparable net benefit for any prostate cancer and a slightly greater net benefit for significant prostate cancer using the ERSPC-RC. CONCLUSIONS: In our independent external validation, both updated RCs showed less optimistic performance compared with their original reports, particularly for the prediction of any prostate cancer. Risk prediction of significant prostate cancer, which is important to avoid unnecessary biopsies and reduce over-diagnosis and overtreatment, was better for both RCs and slightly superior using the ERSPC-RC.
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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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".