Testing the most stringent criteria for selection of candidates for active surveillance in patients with low‐risk prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To test the ability of two of the most stringent criteria used to identify patients with low-risk prostate cancer suitable for active surveillance (AS) to correctly exclude patients with unfavourable prostate cancer characteristics. PATIENTS AND METHODS: The study included 874 consecutive patients treated with radical prostatectomy (RP). We selected patients who could have been selected for AS according to the van den Bergh et al. and the Carter et al. criteria. We analysed the rates of advanced disease in these patients, defined as presence of either extracapsular extension (ECE), seminal vesicle invasion (SVI), lymph node invasion (LNI) and Gleason sum of 8-10 or 7-10. RESULTS: Of 874 patients, 85 (9.7%) and 61 (6.9%) patients, respectively, qualified for AS according to the tested criteria. Within the van den Bergh et al. candidates, 5.9, 1.2, 1.2 and 1.2% of patients, respectively, showed ECE, SVI, LNI and high-grade Gleason sum 8-10 at pathology. Within the Carter et al. candidates, 3.3, 0, 3.3 and 0% of patients, respectively, showed ECE, SVI, LNI and high-grade Gleason sum 8-10. The cumulative rate of unfavourable characteristics was 7.1 and 3.3%. The rate increased to 28.2 and 27.9%, respectively, when Gleason sum 7 was considered as an unfavourable prostate cancer. CONCLUSIONS: The use of the strictest criteria for AS inclusion identified 7-10% of the men in our cohort of men undergoing RP, as men that would have been eligible for AS. Among this small proportion, between 3.3 and 7.1% of patients harboured unfavourable prostate cancer characteristics. The clinical implications of these misclassification rates remain to be determined.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".