Perioperative Complications and In-Hospital Mortality in Paraplegic Radical Cystectomy Patients
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to test for the association between paraplegia and perioperative complications as well as in-hospital mortality after radical cystectomy (RC) for non-metastatic bladder cancer. METHODS: Perioperative complications and in-hospital mortality were tabulated in RC patients with or without paraplegia in the National Inpatient Sample (2000-2019). RESULTS: Of 25,527 RC patients, 185 (0.7%) were paraplegic. Paraplegic RC patients were younger (≤70 years of age; 75 vs. 53%), more frequently female (28 vs. 19%), and more frequently harbored Charlson Comorbidity Index ≥3 (56 vs. 18%). Of paraplegic vs. non-paraplegic RC patients, 141 versus 15,112 (76 vs. 60%) experienced overall complications, 38 versus 2794 (21 vs. 11%) pulmonary complications, 36 versus 3525 (19 vs. 14%) genitourinary complications, 33 versus 3087 (18 vs. 12%) intraoperative complications, 21 versus 1035 (11 vs. 4%) infections, and 17 versus 1343 (9 vs. 5%) wound complications, while 62 versus 6267 (34 vs. 25%) received blood transfusions, 47 versus 3044 (25 vs. 12%) received critical care therapy (CCT), and intrahospital mortality was recorded in 13 versus 456 (7.0 vs. 1.8%) patients. In multivariable logistic regression models, paraplegic status independently predicted higher overall CCT use (odds ratio [OR] 2.1, p < 0.001) as well as fourfold higher in-hospital mortality (p < 0.001), higher infection rate (OR 2.5, p < 0.001), higher blood transfusion rate (OR 1.45, p = 0.009), and higher intraoperative (OR 1.56, p = 0.02), wound (OR 1.89, p = 0.01), and pulmonary (OR 1.72, p = 0.004) complication rates. CONCLUSION: Paraplegic patients contemplating RC should be counseled about fourfold higher risk of in-hospital mortality and higher rates of other untoward effects.
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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 | 0.003 |
| 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 | 0.000 | 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".