Examining the location and cause of death within 30 days of radical prostatectomy
Bibliographic record
Abstract
OBJECTIVES: To better characterize the cause and location of death after radical prostatectomy (RP), as early mortality is relatively uncommon after RP, with little known about the cause of death among men who die within 30 days of RP, and the trend toward earlier discharge after surgery means that a greater proportion of early mortality after RP may occur out of hospital. PATIENTS AND METHODS: Using the Ontario Cancer Registry, we identified 11,010 men (mean age 68 years) who had a RP in the province of Ontario between 1990 and 1999. We identified the occurrence and location of all deaths within 30 days of RP. The cause of death was obtained from death certificate information. Logistic regression was used to examine factors (age, comorbidity, year of surgery) associated with the location of death. RESULTS: Of the 11,010 men, 53 died within 30 days of RP (0.5%); of these 53 men, 28 (53%) died in hospital. Neither age, comorbidity nor year of surgery were significantly associated with location of death (P > 0.05). Major causes of death included cardiovascular disease (38%) and pulmonary embolism (13%). More than half of the patients who died out of hospital had an unknown cause of death. CONCLUSIONS: Almost half of all deaths within 30 days of RP occur out of hospital; the two most common causes of death are potentially preventable. More detailed cause-of-death information may help to identify opportunities for prevention.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".