Readmission Rates of Patients Discharged against Medical Advice: A Matched Cohort Study
Bibliographic record
Abstract
OBJECTIVE: We compared the readmission rates and the pattern of readmission among patients discharged against medical advice (AMA) to control patients discharged with approval over a one-year follow-up period. METHODS: A retrospective matched-cohort study of 656 patients(328 were discharged AMA) who were followed for one year after their initial hospitalization at an urban university-affiliated teaching hospital in Vancouver, Canada that serves a population with high prevalence of addiction and psychiatric disorders. Multivariate conditional logistic regression was used to examine the independent association of discharge AMA on 14-day related diagnosis hospital readmission. We fit a multivariate conditional negative binomial regression model to examine the readmission frequency ratio between the AMA and non-AMA group. PRINCIPAL FINDINGS: AMA patients were more likely to be homeless (32.3% vs. 11%) and have co-morbid conditions such as psychiatric illnesses, injection drug use, HIV, hepatitis C and previous gastrointestinal bleeding. Patients discharged AMA were more likely to be readmitted: 25.6% vs. 3.4%, p<0.001 by day 14. The AMA group were more likely to be readmitted within 14 days with a related diagnosis than the non-AMA group (Adjusted Odds Ratio 12.0; 95% Confidence Interval [CI]: 3.7-38.9). Patients who left AMA were more likely to be readmitted multiple times at one year compared to the non-AMA group (adjusted frequency ratio 1.6; 95% CI: 1.3-2.0). There was also higher all-cause in-hospital mortality during the 12-month follow-up in the AMA group compared to non-AMA group (6.7% vs. 2.4%, p = 0.01). CONCLUSIONS: Patients discharged AMA were more likely to be homeless and have multiple co-morbid conditions. At one year follow-up, the AMA group had higher readmission rates, were predisposed to multiple readmissions and had a higher in-hospital mortality. Interventions to reduce discharges AMA in high-risk groups need to be developed and tested.
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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 | 0.003 |
| 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 | 0.000 |
| 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".