Re-assessment of 30-, 60- and 90-day mortality rates in non-metastatic prostate cancer patients treated either with radical prostatectomy or radiation therapy
Bibliographic record
Abstract
INTRODUCTION: It is customary to consider deaths that occur within 90 days of surgery as caused by that surgery. However, such practice may overestimate the true short-term mortality rates after radical prostatectomy (RP). Indeed, treatment-unrelated events might affect short-term mortality rates. We assess RP-specific excess short-term mortality. METHODS: We performed a retrospective analysis of a population-based cohort of 59 010 patients (RP = 28 281 and external beam radiation therapy [EBRT] as reference group, n = 30 729) who were treated between 1998 and 2005 for non-metastatic prostate cancer. Using univariate and multivariate logistic regression analyses, we assessed the rates of 30-, 60- and 90-day mortality after either RP or EBRT. RESULTS: Within the cohort, 30-, 60- and 90-day mortality rates were 0.2, 0.5 and 0.6%, and 0.1, 0.4 and 0.6% for RP and EBRT patients, respectively. This resulted in overall 30-, 60, and 90- day mortality differences of 0.1, 0.1 and 0%, respectively. After stratification according to age and Charlson comorbidity index (CCI), the magnitude of these differences increased up to 3.2% in favour of EBRT in patients aged >75 years with CCI ≥2. In multivariable analysis, rates of 30-, 60- and 90- day mortality were 5.2-, 1.8- and 1.3-fold higher after RP than EBRT, respectively. Our study is limited by its non-randomized design. CONCLUSION: Overall, absolute short-term mortality rates after RP are comparable to those of EBRT. The difference decreases over time: 90 days <60 days <30 days. Nonetheless, their magnitude is far from trivial in the elderly and sickest patients.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".