In‐hospital mortality and failure‐to‐rescue rates after radical cystectomy
Bibliographic record
Abstract
OBJECTIVE: To show the underlying variability in peri-operative mortality after radical cystectomy (RC) by analysing failure-to-rescue (FTR) rates, i.e. deaths after complications. MATERIALS AND METHODS: Patients undergoing RC for non-metastatic bladder cancer (BCa) were identified from the Nationwide Inpatient Sample, 1999-2009, resulting in a weighted estimate of 79,972 patients. The FTR rates were assessed according to patient and hospital characteristics, as well as complication type. Generalized linear regression analyses were performed. RESULTS: Overall, 26,740 patients had a complication, corresponding to a FTR rate of 5.5%. Septicaemia (odds ratio [OR]: 13.41, P < 0.001) and cardiac (OR: 3.97, P < 0.001), wound-related (OR: 2.12, P < 0.001), genitourinary (OR: 1.62, P = 0.045) and haematological (OR: 1.78, P = 0.008) complications were associated with FTR. Older age (OR: 1.05, P < 0.001), increasing comorbidities (OR: 1.33, P < 0.001), Medicare (OR: 1.52, P = 0.016), and Medicaid insurance status (OR: 2.10, P = 0.029) were associated with higher odds of FTR. Conversely, increasing hospital volume (OR: 0.992, P = 0.014) reduced the odds of FTR. CONCLUSIONS: Whereas both patient and hospital characteristics were associated with increased odds of FTR, the occurrence of septicaemia and cardiac complications were the most strongly associated with a higher risk of in-hospital mortality.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".