Variation in radiation oncology referral and adjuvant radiotherapy use following prostatectomy: A population-based study of health system performance.
Bibliographic record
Abstract
e16551 Background: Evidence-based guidelines confirm a survival advantage of adjuvant radiotherapy (ART) for prostatectomy (RP) patients with high-risk pathology. The efficacy of deferred salvage RT is under evaluation as an alternative strategy, and guidelines recommend radiation oncology (RO) referral for discussion of options. We report RO referral patterns, ART use, and factors associated with these patterns in a contemporary population-based RP cohort. Methods: Electronic treatment records were linked to Ontario's cancer registry. Multivariable regression was used to evaluate clinical and health systems factors associated with RO referral and ART use ≤ 6 months post-RP. Results: From January to November 2012, 2,663 prostate cancer patients received RP in Ontario. Among 1,261 with adverse pathology, 492 (39%) were referred to RO ≤ 6 months post-RP, of which 51% received ART. Multivariable analysis demonstrated that RO referral was more frequent for cases of T3b/T4 disease [OR 17.87; p < 0.0001], T3a disease [OR 5.24; p < 0.0001], Gleason score 8-10 disease [OR 11.32; p < 0.0001], Gleason score 7 disease [OR 4.18; p < 0.0001], at least one non-apex margin positive [OR 4.20; p < 0.0001], an apex only positive margin [OR 2.60; p < 0.0001], RO referral prior to RP [OR 1.95; p < 0.0001], low RP volume hospitals [OR 2.50; p < 0.0001], and increased distance of patient residence from cancer center [OR 1.73; p = 0.01]. There was wide geographic variation in RO referral rates (range 6%-66%; p < 0.0001). Among patients seen by RO, only T3b/T4 disease [OR 5.37; p < 0.0001], T3a disease [OR 2.72; p < 0.0001], at least one non-apex positive margin [OR 2.81; p < 0.0001], and an apex only positive margin [OR 1.32; p < 0.0001] remained predictive of ART on multivariable analysis. Conclusions: Nonmedical factors are important determinants of whether patients are referred for discussion of ART post-RP. Post-RO consultation, treatment decisions are correlated with pathologic findings. Large inter-center variations persist in referral and treatment post-RP, suggesting that further understanding of the reasons for variation could improve access to potentially curative RT in this setting.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".