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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".