Impact of Decipher on use of post‐operative radiotherapy: Individual patient analysis of two prospective registries
Bibliographic record
Abstract
Objective: To assess the association between Genomic Classifier (GC)-risk group and post-radical prostatectomy treatment in clinical practice. Methods: Two prospective observational cohorts of men with prostate cancer (PCa) who underwent RP in two referral centers and had GC testing post-prostatectomy between 2013 and 2018 were included. The primary endpoint of the study was to assess the association between GC-risk group and time to secondary therapy. Univariable (UVA) and multivariable (MVA) Cox proportional hazards models were constructed to assess the association between GC-risk group and time to receipt of secondary therapy after RP, where secondary therapy is defined as receiving either RT or ADT after RP. Results: A total of 398 patients are included in the analysis. Patients with high-GC risk were more likely to receive any secondary therapy (OR: 6.84) compared to patients with low/intermediate-GC risk. The proportion of high-GC risk patients receiving RT at 2 years post-RP was 31.5%, compared to only 6.3% among the low/intermediate-GC risk patients. Conclusion: This study demonstrates that physicians in routine practice used GC to identify high risk patients who might benefit the most from secondary treatment. As such, GC score was independent predictor of receipt of secondary treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".