Leaving hospital against medical advice among HIV-positive patients.
Bibliographic record
Abstract
BACKGROUND: Hospital discharge against medical advice, especially among substance-abusing populations, is a frustrating problem for health care providers. Because of the high prevalence of injection drug use among HIV-positive patients admitted to hospital in Vancouver, we explored the factors associated with leaving hospital against medical advice in this population. METHODS: We reviewed records for all HIV/AIDS patients admitted to St. Paul's Hospital, Vancouver, between Apr. 1, 1997, and Mar. 1, 1999. After identifying the first ("index") admission during this period, we followed the patients' records for 1 year. Multivariate models were applied to identify the determinants of discharge against medical advice and to estimate the impact of such discharge on readmission rate, readmission frequency and length of stay in hospital. RESULTS: Of 981 index admissions among HIV/AIDS patients, 125 (13%) of the patients left the hospital against medical advice. Departure on the day on which welfare cheques were issued and a history of injection drug use were significant predictors of leaving against medical advice. After adjusting for sex, age, severity of illness, injection drug use and homelessness, we found that patients leaving against medical advice were readmitted more frequently than those who were formally discharged (frequency ratio 1.25, 95% confidence interval [CI] 1.11-1.42), were more likely to be readmitted with a related diagnosis within 30 days (odds ratio 5.00, 95% Cl 3.04-8.24) and had significantly longer lengths of stay in the follow-up period. INTERPRETATION: Discharge against medical advice among HIV-positive patients was associated with frequent readmissions with the same diagnosis. Preventing such discharges is likely to benefit patients (by improving their health status) and the health care system (by reducing unnecessary readmissions).
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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 | 0.004 |
| 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".