Survival benefit of definitive therapy in patients with clinically advanced prostate cancer: estimations of the number needed to treat based on competing‐risks analysis
Bibliographic record
Abstract
OBJECTIVE: To describe the survival benefit associated with radical prostatectomy (RP), as compared with initial observation, in patients with locally advanced prostate cancer (PCa). PATIENTS AND METHODS: Overall, 1382 patients with locally advanced PCa treated with RP or initial observation between 1995 and 2009 were identified from the Surveillance, Epidemiology and End Results Medicare insurance programme-linked database. Patients were matched using propensity-score methodology, then 10-year cancer-specific mortality (CSM) rates were estimated and the number needed to treat (NNT) was calculated. Competing-risks regression analyses tested the relationship between treatment type and CSM. RESULTS: Overall, the 10-year CSM rates were 11.8 and 19.3% for patients treated with RP and initial observation, respectively (P < 0.001). The corresponding 10-year NNT was 13. The 10-year CSM rates for the same treatment groups were 8.9 vs 13.9%, respectively, for Gleason score ≤7, 16.8 vs 27.8%, respectively, for Gleason score 8-10, 10.1 vs 15.8%, respectively, for clinical stage T3a, and 17.0 vs 29.3%, respectively, for T3b/T4, respectively (all P ≤ 0.04). The corresponding NNTs were 20, 9, 17 and 8, respectively. In multivariable analyses, RP was an independent predictor of more favourable CSM rates in all categories (all P ≤ 0.04). In separate sensitivity analyses, no differences were recorded when patients treated with radiotherapy were compared with those receiving RP (P = 0.4). Conversely, patients undergoing initial observation had a higher risk of CSM compared with those treated with radiotherapy (P = 0.03). CONCLUSIONS: RP leads to a significant survival advantage compared with observation in patients with locally advanced disease. The highest benefit was observed in patients with T3b/T4 and Gleason score 8-10 disease.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".