COEXISTING PROSTATE CANCER FOUND AT THE TIME OF HOLMIUM LASER ENUCLEATION OF THE PROSTATE FOR BENIGN PROSTATIC HYPERPLASIA: PREDICTING ITS PRESENCE AND GRADE IN ANALYZED TISSUE
Bibliographic record
Abstract
Abstract Objective: To determine the incidence of prostate cancer identified on holmium laser enucleation of the prostate (HoLEP) specimens and evaluate variables associated with prostate cancer identification. Patients and Methods: All patients undergoing HoLEP between 1998 and 2013 were identified. Patients with a known history of prostate cancer were excluded. Multivariable logistic regression assessed variables associated with identification of prostate cancer on HoLEP specimens and Gleason 7 or higher prostate cancer among the malignant cases. The Gleason grade was used as a proxy for disease severity. Each of the models was adjusted for age, preoperative prostate-specific antigen (PSA), and HoLEP specimen weight. Results: The cohort comprised 1272 patients, of whom 103 (8.1%) had prostate cancer identified. Prostate cancer cases had higher pre-HoLEP PSA (p=0.06) but lower HoLEP specimen weight (p=0.01). On multivariate logistic regression, age and preoperative PSA were associated with increased odds...
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.007 | 0.001 |
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".