Radiotherapy after Radical Prostatectomy: Treatment Recommendations Differ between Urologists and Radiation Oncologists
Bibliographic record
Abstract
PURPOSE: There is no consensus on optimal use of radiotherapy following radical prostatectomy. The purpose of this study was to describe opinions of urologists and radiation oncologists regarding adjuvant and salvage radiotherapy following radical prostatectomy. METHODS: Urologists and genitourinary radiation oncologists were solicited to participate in an online survey. Respondent characteristics included demographics, training, practice setting, patient volume/experience, and access to radiotherapy. Participant practice patterns and attitudes towards use of adjuvant and salvage radiotherapy in standardized clinical scenarios were assessed. RESULTS: One hundred and forty-six staff physicians participated in the survey (104 urologists and 42 genitourinary radiation oncologists). Overall, high Gleason score (Gleason 7 vs. 6, RR 1.37 95% CI 1.19-1.56, p<0.0001 and Gleason 8-10 vs. 6, RR 1.56 95% CI 1.37-1.78, p<0.0001), positive surgical margin (RR 1.43 95% CI 1.26-1.62, p<0.0001), and extraprostatic tumour extension (RR 1.16 95% CI 1.05-1.28, p<0.002) conferred an increased probability of recommending adjuvant radiotherapy. Radiation oncologists were more likely to recommend adjuvant radiotherapy across all clinical scenarios (RR 1.48, 95% CI 1.39, 1.60, p <0.001). Major differences were found for patients with Gleason 6 and isolated positive surgical margin (radiotherapy selected by 21% of urologists vs. 70% of radiation oncologists), and patients with extraprostatic extension and negative surgical margins (radiotherapy selected by 18% of urologist vs. 57% of radiation oncologists). CONCLUSIONS: Urologists and radiation oncologists frequently disagree about recommendation for post-prostatectomy adjuvant radiotherapy. Since clinical equipoise exists between adjuvant versus early salvage post-operative radiotherapy, support of clinical trials comparing these approaches is strongly encouraged.
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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 | 0.006 | 0.029 |
| 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 | 0.000 | 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".