Risk of Comorbidities on Postoperative Outcomes in Patients With Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: The effect of comorbidities on postoperative outcomes in patients with inflammatory bowel disease (IBD) has not been explored adequately. We evaluated the prevalence of comorbidities and their effect on postoperative outcomes after an IBD-related operation. METHODS: The Nationwide Inpatient Sample database was used to identify 35 588 patients with IBD who underwent an IBD-related operation from January 1, 1995, through December 31, 2005. The presence of comorbid illness was assessed using the Elixhauser index. Multiple logistic regression analysis was performed to evaluate the effect of comorbidities on mortality rate after adjusting for age, sex, race, health insurance status, and admission type. Linear regression models were used to evaluate health care resource use. RESULTS: Postoperative mortality was 1.9%. As the number of comorbidities increased (ie, 0, 1, 2, or ≥3), postoperative mortality increased (0.4%, 1.5%, 3.3%, and 7.9%, respectively). Congestive heart failure (odds ratio, 3.50 [95% confidence interval, 2.63-4.62]), liver disease (3.15 [2.00-4.97]), thromboembolic disease (4.19 [3.37-5.21]), and renal disease (8.74 [5.44-14.05]) were associated with a significant increase in mortality rate. Comorbidities associated with an increased risk of mortality also were associated with a significant increase in length of stay and hospital charges. CONCLUSIONS: Comorbidities were common in patients with IBD and they significantly increased the risk of postoperative mortality and health care use in patients with IBD.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".