Impact of genomic risk scores on treatment decisions following radical prostatectomy in a prospective Medicare registry.
Bibliographic record
Abstract
72 Background: Prostate cancer patients and providers confront uncertainty as they consider adjuvant or salvage radiation therapy (ART, SRT) after radical prostatectomy (RP). We prospectively evaluated the impact of the Decipher RP test, which predicts metastasis risk after RP, on decision-making for postoperative radiation therapy. Methods: Between October 2016 and May 2017, 1,319 patients treated with RP and considering ART or SRT were enrolled into a Medicare Certification and Training Registry (CTR). Providers submitted a management recommendation based on initial clinical and pathology findings prior to obtaining the Decipher RP test and again upon receiving test results. Only Medicare patients that met the Local Coverage Determination inclusion criteria (i.e., non-organ confined prostate cancer or positive margins or rising PSA) and whose provider was certified in the CTR registry were included in the analysis. Results: Based on clinical variables alone, treatment was recommended for 26% of adjuvant and 19% of salvage patients. Obtaining a Decipher score, changed treatment recommendations in 34% (95% CI 30-39%) and 28% (95% CI 19-38%) of men considering adjuvant or salvage therapy respectively. Among men considering ART, 9% of Decipher low risk patients and 45% of Decipher high-risk patients were recommended treatment. Multivariable logistic regression demonstrated that – independent of pathology risk factors, a high-risk Decipher score was associated with an odds ratio of 7.3 (95% CI 3.9-14.2 p < 0.001) in the adjuvant and 5.5 (95% CI, 1.3-27.8, p = 0.026) in the salvage setting. Conclusions: A prospective CTR demonstrated that use of Decipher resulted in significant changes in treatment decisions for Medicare beneficiaries with PCa considering adjuvant and salvage therapies. Ongoing prospective studies aim at determining how increased use of therapy in men with high Decipher risk impacts oncologic outcomes and whether decreased use in Decipher low risk individuals improves health related quality of life without harming patient survival.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".