Impact of academic affiliation on radical cystectomy outcomes in North America: A population-based study
Bibliographic record
Abstract
BACKGROUND: : The objective of this study was to examine the rates of blood transfusions, prolonged length of stay, intraoperative and postoperative complications, as well as in-hospital mortality, stratified according to institutional academic status in patients undergoing radical cystectomy (RC). METHODS: : Within the Health Care Utilization Project Nationwide Inpatient Sample (NIS), we focused on patients in whom RC was performed between 1998 and 2007. Multivariable logistic regression analyses were fitted to predict the likelihood of blood transfusions, prolonged length of stay, intraoperative and postoperative complications, and in-hospital mortality. Covariates included age, race, gender, Charlson Comorbidity Index (CCI), hospital region, insurance status, annual hospital caseload (AHC), year of surgery and urinary diversion. RESULTS: : Overall, 12 262 patients underwent RC. Of those, 7892 (64.4%) were from academic institutions. Patients treated at academic institutions were younger and healthier at baseline (all p < 0.001). RCs performed at academic institutions were associated with fewer postoperative complications (28.8% vs. 32.9%, p < 0.001), shorter length of stay (54.0% vs. 56.2%, p = 0.02) and lower in-hospital mortality rates (2.1 vs. 3.0%, p = 0.002). In multivariable analyses, patients who underwent RC at an academic hospital were 12% less likely to succumb to postoperative complications (odds ratio=0.88, p = 0.003). INTERPRETATION: : Even after adjusting for AHC, RCs performed at academic institutions are associated with better postoperative outcomes than RCs performed at non-academic institutions. From a public health prospective, performing RCs at academic institutions may help reduce costs associated with the management of complications and prolonged length of stay.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".