Comparison of risk calculators from the Prostate Cancer Prevention Trial and the European Randomized Study of Screening for Prostate Cancer in a contemporary Canadian cohort
Bibliographic record
Abstract
Study Type – Prognosis (inception cohort) Level of Evidence 1b OBJECTIVE • To compare the Prostate Cancer Prevention Trial Risk Calculator (PCPT‐RC) and European Randomized Study of Screening for Prostate Cancer Risk Calculator (ERSPC‐RC) in a single‐institution Canadian cohort. PATIENTS AND METHODS • At Princess Margaret Hospital, 982 consecutive patients with PCPT‐RC and ERSPC‐RC covariables were prospectively catalogued before prostate biopsy for suspicion of prostate cancer (PCa). • Receiver–operating characteristic (ROC) curves were generated for each calculator and prostate‐specific antigen (PSA). • Comparisons by area under the curve (AUC) and calibration plots were performed. • Predictors of PCa were identified by univariable and multivariable logistic regression. RESULTS • PCa was detected in 46% and high‐grade (HG) PCa (Gleason ≥4) in 23% of subjects with a median PSA level of 6.02 ng/mL. • Multivariable analysis identified transrectal ultrasonography nodule, prostate volume and PSA as the most important predictors of PCa and HG PCa. • ROC curve analysis showed that the ERSPC‐RC (AUC = 0.71) outperformed the PCPT‐RC (AUC = 0.63) and PSA (AUC = 0.55), for PCa prediction, P < 0.001. • The PCPT‐RC was better calibrated in the higher prediction range (40–100%) than the ERSPC‐RC, whereas the ERSPC‐RC had better calibration and avoided more biopsies in the lower risk range (0–30%). • Discrimination of the ERSPC‐RC continued to be superior to the PCPT‐RC when the cohort was stratified by different clinical variables. CONCLUSIONS • The ERSPC‐RC had better discrimination for predicting PCa compared to the PCPT‐RC in this Canadian cohort. • Calibration would need to be improved to allow routine use of the ERSPC‐RC in Canadian practice. What’s known on the subject? and What does the study add? The European Randomized Study of Screening for Prostate Cancer risk calculator (ERSPC‐RC) has been validated in a European population and shown to outperform the Prostate Cancer Prevention Trial risk calculator (PCPT‐RC) for predicting prostate cancer. However, the ERSPC‐RC has not been validated in North America where the PCPT‐RC has been extensively validated. This study is the first to compare these calculators in non‐European patient cohort showing better performance of the ERSPC‐RC, but poor calibration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".