Perioperative complications and in-hospital mortality in radical cystectomy patients with heart-valve replacement
Bibliographic record
Abstract
PURPOSE: To assess in-hospital mortality and complication rates after radical cystectomy (RC) in patients with history of heart-valve replacement. MATERIALS AND METHODS: Using the National Inpatient Sample (2000-2019), non-metastatic bladder cancer patients undergoing RC were stratified according to history of heart-valve replacement. Regression models (RM) predicted hospital outcomes. RESULTS: Of 25,535 RC patients, 250 (1.0%) harbored history of heart-valve replacement. Heart-valve replacement patients were older (median 74 vs. 70 years), more frequently male (87.2 vs. 80.6%), and more frequently had Charlson comorbidity index ≥3 (26.8 vs. 18.9%). In RC patients with history of heart-valve replacement vs. others, 62 vs. 2634 (24.8 vs. 10.4%) experienced cardiac complications, 28 vs. 3092 (11.2 vs. 12.2%) intraoperative complications, 11 vs. 1046 (4.4 vs. 4.1%) infections, <11 vs. 594 (<4.4 vs. 2.3%) perioperative bleeding, <11 vs. 699 (<4.4 vs. 2.8%) vascular complications, 74 vs. 6225 (29.6 vs. 24.7%) received blood transfusions, 37 vs. 3054 (14.8 vs. 12.1%) critical care therapy (CCT), and in-hospital mortality was recorded in <11 vs. 463 (<4.4 vs. 1.8%) patients. In multivariable RM, history of heart-valve replacement independently predicted cardiac complications (odds ratio 2.20, 95% confidence interval 1.62-2.99; p < 0.001). Conversely, no statically significant association was recorded between history of heart-valve replacement and length of stay, estimated hospital cost, intraoperative complications, perioperative bleeding, vascular complications, infections, blood transfusions, CCT use, and in-hospital mortality. CONCLUSIONS: Radical cystectomy patients with history of heart-valve replacement exhibited a 2.2-fold higher risk of cardiac complications, but no other complications, including no significantly higher 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".