A111 CHARACTERISTICS OF HOSPITALIZED PATIENTS WITH GASTROINTESTINAL DISEASE WHO LEAVE AGAINST MEDICAL ADVICE
Bibliographic record
Abstract
Abstract Background Hospitalized inpatients who leave against medical advice (AMA) may have incomplete care, resulting in readmission and increased healthcare costs. Limited evidence exists in patients diagnosed with a primary gastrointestinal (GI) disease who leave AMA. Identifying patients with GI diagnoses who leave AMA may inform risk stratification to determine which patients are at risk of unplanned discharge. Aims Identify characteristics in patients diagnosed with GI diseases who leave AMA. Methods A retrospective evaluation was conducted using the National Inpatient Sample (NIS) from 2016 to 2020. The NIS provides national-level estimates of cost, quality, and outcomes from hospitalizations in the United States. We used International Classification of Diseases, Tenth Revision diagnostic coding to identify patients admitted with a primary GI problem. The proportion of patients who left AMA for each diagnosis was determined (Figure 1). A multivariable logistic regression was performed to determine factors associated with an AMA discharge, adjusting for age, sex, elective or weekend admission, primary payment method, race, hospital setting, geography, income, Elixhauser comorbidity index, alcohol and/or drug abuse and depression status. Results Between 2016 and 2020, 1.61% of patients with a GI primary problem left the hospital AMA. Characteristics associated with leaving AMA included: older age, female sex, non-white race, rural hospital admission, higher income and having comorbid conditions (Table 1). Patients with a history of alcohol abuse were more likely to leave AMA, while patients with a history of drug abuse were less likely to leave AMA. Conclusions Approximately 1 in 60 patients admitted with a primary GI problem leave hospital AMA and we identify both structural and patient-level factors associated with unplanned discharge. Table 1: Factors Associated With AMA Discharge For GI-related Diagnoses Funding Agencies None
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".