Effect of decipher test on adjuvant treatment decision-making among men with high-risk pathology at radical prostatectomy: Results from a multicenter prospective PRO-IMPACT study.
Bibliographic record
Abstract
24 Background: The decision to provide adjuvant therapy to men with high risk pathology after radical prostatectomy (RP) is confounded by tremendous uncertainty. We prospectively evaluated the impact of the Decipher test, which predicts metastases after RP, on men and providers decision quality. Methods: 150 adjuvant pts were enrolled by 43 urologists from 19 practices. Pts with pathologic T3 stage or positive surgical margins (SM+) after RP were included. Participating physicians provided a treatment (Tx) recommendation before and after exposure to Decipher test results. Pts completed validated surveys on health-related quality of life, decisional conflict, and PCa-related anxiety. Results: Median patient age at RP was 64 years; 67% and 50% had pT3 and SM+ pathology, respectively. Decipher classified 46%, 22% and 32% of men as low-, intermediate- and high-risk, respectively. Pre-Decipher, observation was recommended for 89%. Post-Decipher, 18% (95% CI 12-25%) of Tx recommendations changed. Men’s Decisional Conflict Scale (DCS) scores decreased (indicating higher decision quality) after exposure to Decipher results (median DCS pre-Decipher 25 [IQR 8-44], median DCS post-Decipher 19 [IQR 2-30], p<0.001), with greatest decreases in the subdomains of decision uncertainty and decision support. Low-risk Decipher results experienced a trend toward decreased PCa-specific anxiety (p=0.13) and a significant reduction in fear of PCa recurrence (p=0.02). Physicians’ median DCS scores decreased from 32 [IQR 28-36] to 28 [IQR 12-42] (p<0.001). Decipher results were associated with the decision to pursue ART in an MVA analysis (OR 1.48; 95% CI 1.19-1.85, p<0.001). Conclusions: Observation is the predominantly prescribed management strategy for men with high risk features at RP. Knowledge of Decipher results was associated with Tx decision-making: men at low risk for metastasis had higher rates of observation recommendations and men at high risk had higher rates of ART recommendations. Decision quality was improved and PCa-specific anxiety was decreased for men exposed to Decipher results. Clinical trial information: NCT02080689.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".