Prospective randomized trial of genomic classifier impact on treatment decisions in patients at high risk of recurrence following radical prostatectomy (G-MINOR).
Bibliographic record
Abstract
TPS154 Background: Approximately 30% of patients will have ≥pT3 disease and/or positive surgical margins at radical prostatectomy (RP), indicating a high risk of local recurrence. While current guidelines recommend consideration of adjuvant radiotherapy (aRT) in this setting, < 10% undergo aRT. The Decipher assay is a novel, tissue-based genomic classifier (GC) developed and validated in the post-RP setting as a predictor of metastasis. Current retrospective evidence suggests that patients with a high GC score may benefit from aRT, while observation may be safe for those with a lower GC score. However, there are no randomized prospective data evaluating the clinical utility of biomarkers in men with adverse features after RP. Here we see to determine the impact of GC test results on adjuvant treatment decisions for high-risk post-RP patients vs. clinical factors alone. Methods: Genomics in Michigan ImpactiNg Observation or Radiation (G-MINOR) is a 4-year (12-month enrollment, 3-year follow-up) prospective, cluster-crossover, unblinded, study of 350 subjects from twelve Urology practices in the Michigan Urological Surgery Improvement Collaborative (MUSIC). MUSIC is a physician-led quality improvement consortium nearly all academic and community urology practices within the state of Michigan. Each clinical center participating in this trial will be randomly assigned to either a Genomic Classifier (GC)-based strategy or control arm for a period of 3 months. Patients in both arms will receive a predicted risk of recurrence based on a validated clinical nomogram, the CAPRA-S score, enabling a head-to-head comparison of the Decipher assay with a freely-available validated prognostic tool. Random assignments will be generated centrally by a study statistician and provided to centers immediately before commencing enrollment in each 3-month period. Each center will have two GC and two UC enrollment periods, maintaining study-wide balance and blinding of assignments in subsequent periods. Patients will be followed for receipt of adjuvant therapy as well as oncologic (recurrence, metastasis, and death) and patient-reported quality of life. Clinical trial information: NCT02783950.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".