A population-based analysis of leaving the hospital against medical advice: incidence and associated variables
Bibliographic record
Abstract
BACKGROUND: Prior studies of patients leaving hospital against medical advice (AMA) have been limited by not being population-based or assessing only one type of patient. METHODS: We used administrative data at the Manitoba Centre for Health Policy to evaluate all adult residents of Manitoba, Canada discharged alive from acute care hospitals between April 1, 1990 and February 28, 2009. We identified the rate of leaving AMA, and used multivariable logistic regression to identify socio-demographic and diagnostic variables associated with leaving AMA. RESULTS: Of 1,916,104 live hospital discharges, 21,417 (1.11%) ended with the patient leaving AMA. The cohort contained 610,187 individuals, of whom 12,588 (2.06%) left AMA once and another 2986 (0.49%) left AMA more than once. The proportion of AMA discharges did not change over time. Alcohol and drug abuse was the diagnostic group with the highest proportion of AMA discharges, at 11.71%. Having left AMA previously had the strongest association with leaving AMA (odds ratio 170, 95% confidence interval 156-185). Leaving AMA was more common among men, those with lower average household incomes, histories of alcohol or drug abuse or HIV/AIDS. Major surgical procedures were associated with a much lower chance of leaving the hospital AMA. CONCLUSIONS: The rate of leaving hospital AMA did not systematically change over time, but did vary based on patient and illness characteristics. Having left AMA in the past was highly predictive of subsequent AMA events.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".