Hospital Volume and Other Risk Factors for In-Hospital Mortality among Diverticulitis Patients: A Nationwide Analysis
Bibliographic record
Abstract
BACKGROUND: Previous studies have found that a higher volume of colorectal surgery was associated with lower mortality rates. While diverticulitis is an increasingly common condition, the effect of hospital volume on outcomes among diverticulitis patients is unknown. OBJECTIVE: To evaluate the relationship between hospital volume and other factors on in-hospital mortality among patients admitted for diverticulitis. METHODS: Data from the Nationwide Inpatient Sample (years 1993 to 2008) were analyzed to identify 822,865 patients representing 4,108,726 admissions for diverticulitis. Hospitals were divided into quartiles based on the volume of diverticulitis cases admitted over the study period, adjusted for years contributed to the dataset. Mortality according to hospital volume was modelled using logistic regression adjusting for age, sex, race, comorbidities, health care insurance, admission type, calendar year, colectomy, disease severity and clustering. Risk estimates were expressed as adjusted ORs with 95% CIs. RESULTS: Patients at high-volume hospitals were more likely to be admitted emergently, undergo surgical treatment and have more severe disease. In-hospital mortality was higher among the lowest quartile of hospital volume compared with the highest volume (OR 1.13 [95% CI 1.05 to 1.21]). In-hospital mortality was increased among patients admitted emergently (OR 2.58 [95% CI 2.40 to 2.78]) as well as those receiving surgical treatment (OR 3.60 [95% CI 3.42 to 3.78]). CONCLUSIONS: Diverticulitis patients admitted to hospitals with a low volume of diverticulitis cases had an increased risk for death compared with those admitted to high-volume centres.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".