Critical assessment of tools to predict clinically insignificant prostate cancer at radical prostatectomy in contemporary men
Bibliographic record
Abstract
BACKGROUND: Overtreatment of prostate cancer (PCa) is a concern, especially in patients who might qualify for the diagnosis of insignificant prostate cancer (IPCa). The ability to identify IPCa prior to definitive therapy was tested. METHODS: In a cohort of 1132 men a nomogram was developed to predict the probability of IPCa. Predictors consisted of prostate-specific antigen (PSA), clinical stage, biopsy Gleason sum, core cancer length and percentage of positive biopsy cores (percent positive cores). IPCa was defined as organ-confined PCa (OC) with tumor volume (TV) <0.5 cc and without Gleason 4 or 5 patterns. Finally, an external validation of the most accurate IPCa nomogram was performed in the same group. RESULTS: IPCa was pathologically confirmed in 65 (5.7%) men. The 200 bootstrap-corrected predictive accuracy of the new nomogram was 90% versus 81% for the older nomogram. However, in cutoff-based analyses of patients who were qualified by our and the older nomograms as high probability for IPCa, respectively 63% and 45% harbored aggressive PCa variants at radical prostatectomy (Gleason score 7-10, ECE, SVI, and/or LNI). CONCLUSIONS: Despite a high accuracy, currently available models for prediction of IPCa are incorrect in 10% to 20% of predictions. The rate of misclassification is even further inflated when specific cutoffs are used. As a consequence, extreme caution is advised when statistical tools are used to assign the diagnosis of IPCa.
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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 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".