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.
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 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 | 0.000 | 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".