Influence of a genomic classifier on post-operative treatment decisions in high-risk prostate cancer patients: results from the PRO-ACT study
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of an individualized genomic classifier (GC) test, for predicting metastasis following radical prostatectomy (RP), on urologists' adjuvant treatment decisions when caring for high-risk patients. PATIENTS AND METHODS: Data were submitted by US board-certified urologists in community practices (n = 15), who ordered the GC test for 146 prostate cancer patients with adverse pathologic features following RP (i.e., pathologic stage pT3 or positive surgical margins). Treatment recommendations were reported using an electronic data collection instrument, before and after reviewing the GC test report. Physicians also completed a Decision Conflict Scale (DCS), a decisional conflict measure, to assess their confidence with their treatment recommendations. RESULTS: Over 60% of high-risk patients were re-classified as low risk after review of the GC test results. Overall, adjuvant treatment recommendations were modified for 30.8% (95% CI = 23-39%) of patients. With GC test results, 42.5% of patients who were initially recommended adjuvant therapy were subsequently recommended observation. Although the number of patients recommended adjuvant therapy remained the same before and after review of the GC test results, it did influence patient treatment strategies. Multivariable analysis confirmed GC risk was the only significant predictor of treatment recommendations (OR = 4.04; 95% CI = 2.36, 6.92; p < 0.0001). Decisional conflict with regard to adjuvant treatment decisions was significantly less with the use of the GC test (p < 0.0001). CONCLUSIONS: Information on individualized metastasis risk based on a patient's tumor biology, with use of the GC test, significantly changed urologists' adjuvant treatment recommendations for post-operative patients with prostate cancer, who were at high risk of metastasis. Namely, the results of this study provide evidence for the utility of the GC test, and show it may guide use of adjuvant radiation.
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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 | 0.001 | 0.004 |
| 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".