Preoperative parameters to predict incidental (T1a and T1b) prostate cancer
Bibliographic record
Abstract
INTRODUCTON: Prostate cancer has been found incidentally in transurethral resection of the prostate (TURP) specimens without prior diagnosis in 5% to 13% of the patients. We evaluated whether incidental prostate cancer (stages T1a and T1b) could be predicted preoperatively. METHODS: TURP was performed in 307 patients between 2006 and 2011. Patient age, prostate-specific antigen (PSA) level, total prostate volume, transitional zone volume, PSA density, history of needle biopsy, and pathological diagnosis on TURP specimen were assessed. We analyzed the association between these parameters and prostate cancer detection. RESULTS: Incidental prostate cancer was found in 31 patients (10.1%), and 13 cases (4.2%) had cancer with T1b and/or Gleason ≥7. Multivariate analysis demonstrated that age ≥75 years (odds ratio [OR] 2.58, p = 0.022), prostate volume ≤50 cc (OR 4.11, p < 0.001), and the absence of preoperative needle biopsy despite PSA ≥4 ng/mL (OR 2.65, p = 0.046) were independent risk factors. In patients who had 2 or 3 of these risk factors, incidental prostate cancer and cancer with T1b and/or Gleason ≥7 were observed in 25% to 50% and 16% to 25% cases, respectively. CONCLUSIONS: Older patient age, small prostate volume, and the absence of previous needle biopsy (despite a high PSA level) might be independent risk factors for detecting incidental prostate cancer, although external validation is warranted to confirm our results.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".