Survival after radical prostatectomy versus radiation therapy in clinical node‐positive prostate cancer
Bibliographic record
Abstract
AIM: To compare overall mortality (OM), cancer-specific mortality (CSM), and other cause mortality (OCM) rates between radical prostatectomy (RP) versus radiotherapy (RT) in clinical node-positive (cN1) prostate cancer (PCa). MATERIALS AND METHODS: Within Surveillance, Epidemiology, End Results (SEER) (2004-2016), we identified 4685 cN1 PCa patients, of whom 3589 (76.6%) versus 1096 (24.4%) were treated with RP versus RT. After 1:1 propensity score matching (PSM), Kaplan-Meier plots and Cox regression models tested the effect of RP versus RT on OM, while cumulative incidence plots and competing-risks regression (CRR) models addressed CSM and OCM between RP and RT patients. All analyses were repeated after the inverse probability of treatment weighting (IPTW). For CSM and OCM analyses, the propensity score was used as a covariate in the regression model. RESULTS: Overall, RT patients were older, harbored higher prostate-specific antigen values, higher clinical T and higher Gleason grade groups. PSM resulted in two equally sized groups of 894 RP versus 894 RT patients. After PSM, 5-year OM, CSM, and OCM rates were, respectively, 15.4% versus 25%, 9.3% versus 17%, and 6.1% versus 8% for RP versus RT (all p < 0.001) and yielded respective multivariate hazard ratios (HRs) of 0.63 (0.52-0.78, p < 0.001), 0.66 (0.52-0.86, p < 0.001), 0.71 (0.5-1.0, p = 0.05), all favoring RP. After IPTW, Cox regression models yielded HR of 0.55 (95% confidence interval [CI] = 0.46-0.66) for OM, and CRR yielded HRs of 0.49 (0.34-0.70) and 0.54 (0.36-0.79) for, respectively, CSM and OCM, all favoring RP (all p < 0.001). CONCLUSIONS: RP may hold a CSM advantage over RT in cN1 PCa patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".