Early radiotherapy after radical prostatectomy improves cancer‐specific survival only in patients with highly aggressive prostate cancer: Validation of recently released criteria
Bibliographic record
Abstract
OBJECTIVES: To test the effect of radiotherapy administered within 6 months after radical prostatectomy on cancer-specific mortality in prostate cancer patients after stratification according to a risk score. METHODS: Overall, 7616 patients with pT3/4 N0/1 prostate cancer treated with radical prostatectomy between 1995 and 2009 within the Surveillance Epidemiology and End Results Medicare-linked database were included in the study. Competing-risks regression models were carried out to test the effect of early radiotherapy on cancer-specific mortality in the entire cohort, and after stratifying patients according to the risk score based on the number and nature of adverse pathological characteristics (Gleason score 8-10; pT3b/4, lymph node invasion). RESULTS: The risk score was associated with increasing 5- and 10-year cancer-specific mortality rates (P < 0.001). When considering only patients with a risk score ≥ 2, 5- and 10-year cancer-specific mortality rates were significantly lower for individuals undergoing early radiotherapy compared with their counterparts not receiving early radiotherapy (2.9 and 6.9 vs 5.7 and 16.2%, respectively; P = 0.002). The corresponding number required to treat to prevent one death from prostate cancer at 10-year follow up was 10. Early radiotherapy was not associated with lower cancer-specific mortality rates overall and in patients with a risk score <2. This was confirmed in multivariable analyses, where early radiotherapy decreased the risk of cancer-specific mortality only in patients with a risk score ≥ 2 (P ≤ 0.02). CONCLUSIONS: The presence of two or more of the following pathological features might be used to identify patients who benefit from early radiotherapy: Gleason score 8-10, pT3b/4 and lymph node invasion.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".