The novel nomogram of Gleason sum upgrade: Possible application for the eligible criteria of low dose rate brachytherapy
Bibliographic record
Abstract
OBJECTIVE: To examine the rate of Gleason sum upgrading (GSU) from a sum of 6 to a Gleason sum of ≥7 in patients undergoing radical prostatectomy (RP), who fulfilled the recommendations for low dose rate brachytherapy (Gleason sum 6, prostate-specific antigen ≤10 ng/mL, clinical stage ≤T2a and prostate volume ≤50 mL), and to test the performance of an existing nomogram for prediction of GSU in this specific cohort of patients. METHODS: The analysis focused on 414 patients, who fulfilled the European Society for Therapeutic Radiation and Oncology and American Brachytherapy Society criteria for low dose rate brachytherapy (LD-BT) and underwent a 10-core prostate biopsy followed by RP. The rate of GSU was tabulated and the ability of available clinical and pathological parameters for predicting GSU was tested. Finally, the performance of an existing GSU nomogram was explored. RESULTS: The overall rate of GSU was 35.5%. When applied to LD-BT candidates, the existing nomogram was 65.8% accurate versus 70.8% for the new nomogram. In decision curve analysis tests, the new nomogram fared substantially better than the assumption that no patient is upgraded and better than the existing nomogram. CONCLUSIONS: GSU represents an important issue in LD-BT candidates. The new nomogram might improve patient selection for LD-BT and cancer control outcome by excluding patients with an elevated probability of GSU.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".