Inflammatory bowel disease patients who leave hospital against medical advice: Predictors and temporal trends
Bibliographic record
Abstract
BACKGROUND: Leaving hospital against medical advice (AMA) may have consequences with respect to health-related outcomes; however, inflammatory bowel disease (IBD) patients have been inadequately studied. Thus, we determined the prevalence of self-discharge, assessed predictors of AMA status, and evaluated time trends. METHODS: We analyzed the 1995-2005 Nationwide Inpatient Sample (NIS) to identify 93,678 discharges with a primary diagnosis of IBD admitted to the hospital emergently and did not undergo surgery. We described the proportion of IBD patients who left AMA. Predictors of AMA status were evaluated using a multivariate logistic regression model and temporal trend analyses were performed with Poisson regression models. RESULTS: Between 1995 and 2005, 1.31% of IBD patients left hospitals AMA. Crohn's disease (CD) patients were more likely to leave AMA (adjusted odds ratio [aOR], 1.53; 95% confidence intervals [CI]: 1.30-1.79). Characteristics associated with leaving AMA included: ages 18-34 (aOR, 7.77, 95% CI: 4.34-13.89); male (aOR, 1.75; 95% CI: 1.55-1.99); Medicaid (aOR, 4.55; 95% CI: 3.81-5.43) compared to private insurance; African Americans (aOR, 1.34; 95% CI: 1.09-1.64) compared to white; substance abuse (aOR, 2.75; 95% CI: 2.14-3.54); and psychosis (aOR, 1.55; 95% CI: 1.13-2.14). The incidence rates of self-discharge for CD patients were stable (P > 0.05) between 1995 and 1999, while they significantly (P < 0.0001) increased after 1999. In contrast, AMA rates for UC patients remained stable during the study period. CONCLUSIONS: Approximately 1 in 76 IBD patients admitted emergently for medical management leave the hospital AMA. These were primarily disenfranchised patients who may lack adequate outpatient follow-up.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".