Predictors of Leaving an Inpatient Medical Withdrawal Service Against Medical Advice: A Retrospective Analysis
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to determine the frequency and predictors of patients leaving an inpatient medical withdrawal unit against medical advice (AMA). METHODS: This study used a case-control design to compare patients who were discharged AMA (n = 164) with those who completed treatment (n = 678). Logistic regression analysis was used to determine which variables were independent predictors of patients leaving AMA. RESULTS: We found that being admitted through the emergency department (odds ratio [OR] 3.17, confidence interval [CI] 1.66-6.08), having gamma-hydroxybutyrate (OR 7.61, CI 1.81-32.09) as a primary substance of concern compared to alcohol, and having multiple axis I psychiatric diagnoses (OR 2.20, CI 1.16-4.18) or depression (OR 2.86, CI 1.32-6.17) compared with no psychiatric diagnosis increased the odds of leaving inpatient medical withdrawal AMA. By contrast, not being dependent on nicotine (OR 0.45, CI 0.23-0.88) and increasing time since admission (OR 0.42, CI 0.36-0.48) reduced the odds of leaving AMA. CONCLUSIONS: The findings of this study reveal novel information about patients who leave inpatient medical withdrawal AMA and can inform targeted interventions to prevent vulnerable patients from terminating treatment early and improve healthcare service utilization.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".