Nationwide patterns of hospitalizations to centers with high volume of admissions for inflammatory bowel disease and their impact on mortality
Bibliographic record
Abstract
BACKGROUND: We sought to determine patterns of hospitalizations for inflammatory bowel disease (IBD) to centers that regularly admit high volumes of IBD patients and whether they impacted health outcomes. METHODS: We queried US hospital discharges in the Nationwide Inpatient Sample to identify admissions with a primary diagnosis of Crohn's disease (CD) or ulcerative colitis (UC) between 1998 and 2004. We determined patterns and predictors of hospitalization at high IBD volume admission centers (HIVACs) (>or=145 IBD admissions annually) and assessed their impact on mortality. RESULTS: Over 7 years the proportion of patients admitted to HIVACs increased from 2.3% to 14.8%. IBD patients were less likely to be admitted to an HIVAC if they were insured by Medicare (odds ratio [OR] 0.74; 95% confidence interval [CI]: 0.65-0.83) or Medicaid (OR 0.71; 95% CI: 0.60-0.84), or were uninsured (OR 0.42; 95% CI: 0.30-0.58) compared with those privately insured. Neighborhood income above the national median favored admission to an HIVAC (OR 1.99; 95% CI: 1.46-2.71). In-hospital mortality was lower among HIVACs compared to non-HIVACs (3.5/1000 versus 7.2/1000, P < 0.0001) and was persistent after adjustment for surgery status, age, comorbidity, and health insurance (OR 0.65; 95% CI: 0.49-0.87). When stratified by diagnosis, mortality was reduced at HIVACs among CD (OR 0.58; 95% CI: 0.37-0.90) but not UC admissions. CONCLUSIONS: There is a rising trend in hospitalizations for IBD at HIVACs, which confers mortality benefit for those with CD. Prospective studies are warranted to further explore the impact of these high-volume centers on IBD health outcomes.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".