Predictors of Mortality among Patients Undergoing Colectomy for Ischemic Colitis: A Population-Based, United States Study
Bibliographic record
Abstract
BACKGROUND: Ischemic colitis is a potentially life-threatening condition that can require colectomy for management. OBJECTIVE: To assess independent predictors of mortality following colectomy for ischemic colitis using a nationally representative sample of hospitals in the United States. METHODS: The Nationwide Inpatient Sample was used to identify all patients with a primary diagnosis of acute vascular insufficiency of the colon (International Classification of Diseases, Ninth Revision codes 557.0 and 557.9) who underwent a colectomy between 1993 and 2008. Incidence and mortality are described; multivariate logistic regression analysis was performed to determine predictors of mortality. RESULTS: The incidence of colectomy for ischemic colitis was 1.43 cases (95% CI 1.40 cases to 1.47 cases) per 100,000. The incidence of colectomy for ischemic colitis increased by 3.1% per year (95% CI 2.3% to 3.9%) from 1993 to 2003, and stabilized thereafter. The postoperative mortality rate was 21.0% (95% CI 20.2% to 21.8%). After 1997, the mortality rate significantly decreased at an estimated annual rate of 4.5% (95% CI -6.3% to -2.7%). Mortality was associated with older age, 65 to 84 years (OR 5.45 [95% CI 2.91 to 10.22]) versus 18 to 34 years; health insurance, Medicaid (OR 1.69 [95% CI 1.29 to 2.21]) and Medicare (OR 1.33 [95% CI 1.12 to 1.58]) versus private health insurance; and comorbidities such as liver disease (OR 3.54 [95% CI 2.79 to 4.50]). Patients who underwent colonoscopy or sigmoidoscopy (OR 0.78 [95% CI 0.65 to 0.93]) had lower mortality. CONCLUSIONS: Colectomy for ischemic colitis was associated with considerable mortality. The explanation for the stable incidence and decreasing mortality rates observed in the latter part of the present study should be explored in future studies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".