Clinical predictors of gleason score upgrading
Bibliographic record
Abstract
BACKGROUND: Brachytherapy, active surveillance, and watchful waiting are increasingly being offered to men with low-risk prostate cancer. However, many of these men harbor undetected high-grade disease (Gleason pattern > or =4). The ability to identify those individuals with occult high-grade disease may help guide treatment decisions in this patient cohort. METHODS: The authors identified 175 cases of low-risk prostate cancer treated with radical prostatectomy. By using logistic regression analysis, 11 a priori-defined preoperative risk factors were evaluated for their ability to predict upgrading from Gleason 6 at biopsy to Gleason > or =7 at radical prostatectomy. An internally validated nomogram using all clinical variables was subsequently created to help physicians identify patients who had undetected high-grade disease. RESULTS: A total of 60 (34%) patients were upgraded to high-grade disease. On multivariate analyses, both prostate-specific antigen (PSA) level (P = .02) and the level of pathologist expertise (P = .007) were predictive of upgrading. The predictive nomogram contained these variables plus age, digital rectal examination, transrectal ultrasound results, biopsy scheme applied (sextant vs extended), presence of prostatic intraepithelial neoplasia, prostate gland volume, and percentage of cancer in the biopsy. The nomogram provided acceptable discrimination (C statistic 0.71). CONCLUSIONS: The authors identified significant predictors of upgrading for patients diagnosed with low-risk prostate cancer. A nomogram based on these study findings could help physicians further risk-stratify patients with low-risk prostate cancer before embarking on treatment. Caution should be exercised in recommending nonradical therapy to individuals with a high probability of undetected high-grade disease.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".