Cancer-specific mortality after radical prostatectomy vs external beam radiotherapy in high-risk Hispanic/Latino prostate cancer patients
Bibliographic record
Abstract
PURPOSE: To test for differences in cancer-specific mortality (CSM) rates in Hispanic/Latino prostate cancer patients according to treatment type, radical prostatectomy (RP) vs external beam radiotherapy (EBRT). METHODS: Within the Surveillance, Epidemiology, and End Results database (2010-2016), we identified 2290 NCCN (National Comprehensive Cancer Network) high-risk (HR) Hispanic/Latino prostate cancer patients. Of those, 893 (39.0%) were treated with RP vs 1397 (61.0%) with EBRT. First, cumulative incidence plots and competing risks regression models tested for CSM differences after adjustment for other cause mortality (OCM). Second, cumulative incidence plots and competing risks regression models were refitted after 1:1 propensity score matching (according to age, PSA, biopsy Gleason score, cT-stage, cN-stage). RESULTS: In NCCN HR patients, 5-year CSM rates for RP vs EBRT were 2.4 vs 4.7%, yielding a multivariable hazard ratio of 0.37 (95% CI 0.19-0.73, p = 0.004) favoring RP. However, after propensity score matching, the hazard ratio of 0.54 was no longer statistically significant (95% CI 0.21-1.39, p = 0.2). CONCLUSION: Without the use of strictest adjustment for population differences, NCCN high-risk Hispanic/Latino prostate cancer patients appear to benefit more of RP than EBRT. However, after strictest adjustment for baseline patient and tumor characteristics between RP and EBRT cohorts, the apparent CSM benefit of RP is no longer statistically significant. In consequence, in Hispanic/Latino NCCN high-risk patients, either treatment modality results in similar CSM outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".