Temporal Trends and Predictors of Pancreatitis Patients Who Leave Against Medical Advice: A Nationwide Analysis
Bibliographic record
Abstract
BACKGROUND: Acute pancreatitis is the leading gastrointestinal cause of hospital admissions. Our study aims to determine the trends and predictors of discharge against medical advice (AMA). METHODS: We utilized the Nationwide Inpatient Sample (2003 - 2016) to identify patients admitted with pancreatitis. We compared in-hospital complications and determined predictors of discharge AMA using a multivariate logistic regression. RESULTS: A total of 7,158,894 patients were admitted with pancreatitis. Of those, 199,351 left AMA. Discharge AMA increased over time from 2.3% to 3.2%. Patients who left AMA were more likely to be younger, male, black, and a lower socioeconomic status (SES). They had a greater prevalence of depression, cirrhosis, smoking, drug abuse, and human immunodeficiency virus (HIV) infection. Alcohol use was the most likely etiology of pancreatitis among those leaving AMA. In a multivariate regression, patients more likely to leave AMA included: age 18 - 44, male, and black. Patients with a history of depression, drug abuse, and HIV infection were also more likely to be discharged AMA. CONCLUSIONS: Discharges AMA increased over time. Predictors of AMA include patients who are younger, male, black, lower socioeconomic status, and have a history of depression, HIV infection, alcohol and drug use. Future studies are necessary to examine the reasons for discharge AMA among this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".