What happens to patients who leave hospital against medical advice?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Patients who leave hospital against medical advice (AMA) may be at risk of adverse health outcomes and readmission. In this study we examined rates of readmission and predictors of readmission among patients leaving hospital AMA. METHODS: We prospectively studied 97 consecutive patients who left the general medicine service of an urban teaching hospital AMA. Each patient was matched according to age, sex and primary diagnosis with a control patient who was discharged routinely. Readmission rates were examined using Kaplan-Meier analysis. Regression models were used to test the hypothesis that readmissions among patients discharged AMA followed a biphasic curve. RESULTS: Patients who left AMA were much more likely than the control patients to be readmitted within 15 days (21% v. 3%, p < 0.001). Readmissions occurred at an accelerated pace during the first 15 days, followed by a 75-day period during which readmissions occurred at a rate comparable to that among the control patients. Among the patients who left AMA, being male and having a history of alcohol abuse were significant predictors of readmission within 15 days; however, these characteristics were common among the patients who left AMA. In the Cox proportional hazard models, leaving AMA was the only significant predictor of readmission (adjusted hazard ratio 2.5, 95% confidence interval 1.4-4.4). INTERPRETATION: The significantly increased risk of readmission among general medicine patients who leave hospital AMA is concentrated in the first 2 weeks after discharge. However, it is difficult to identify which patients will likely be readmitted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it