HIV-Positive Injection Drug Users Who Leave the Hospital Against Medical Advice
Bibliographic record
Abstract
BACKGROUND: Leaving the hospital against medical advice has been associated with increased morbidity and readmission. Factors associated with the risk of leaving against medical advice among HIV/AIDS patients or injection drug users have not been examined in detail. OBJECTIVES: To examine the clinical and social factors associated with leaving against medical advice (AMA) from a specialized HIV/AIDS ward among patients who reported a history of injection drug use. METHODS: All patients with a history of injection drug use admitted to the HIV/AIDS ward at St. Paul's Hospital, Vancouver, British Columbia (the largest specialized HIV/AIDS hospital ward in Canada) between April 1997 and October 2000 were reviewed retrospectively. A multivariate logistic regression model utilizing a generalized estimating equation algorithm identified factors associated with leaving the hospital AMA. RESULTS: Of the 1056 hospital admissions to the HIV/AIDS ward by patients with a history of injection drug use, 263 (24.9%) resulted in leaving the hospital AMA. Independent positive predictors of leaving AMA included recent injection drug use (adjusted odds ratio [AOR] = 2.08, 95% confidence interval [CI]: 1.41-3.07) and aboriginal ethnicity (AOR = 1.55, 95% CI: 1.05-2.28). Discharge AMA was also more likely to occur on weekends (AOR = 2.27, 95% CI: 1.49-3.48) and on days when social assistance payments were issued (AOR = 2.95, 95% CI: 1.70-5.10). Factors that independently reduced the odds of hospital discharge AMA included in-hospital methadone use (AOR = 0.49, 95% CI: 0.32-0.76), social support (AOR = 0.33, 95% CI: 0.21-0.51), and older age (per 10-year increment, AOR = 0.56, 95% CI: 0.43-0.73). CONCLUSIONS: Among HIV-positive patients with a history of injection drug use, the odds of leaving the hospital AMA were reduced for subjects who received inpatient methadone treatment, were of older age, or had social supports. Addiction treatment and interventions that enhance social supports in marginalized populations at risk for hospital discharge AMA should be further explored.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".