An examination of discharge against medical advice from brain injury inpatient rehabilitation
Bibliographic record
Abstract
OBJECTIVE: Discharges against medical advice (DAMA) have been considered predictors of adverse outcomes for patients in acute care and psychiatric hospitals. However, little is known about the profile of patients who discharge AMA admitted to rehabilitation hospitals. The aims of this study were to provide a profile of patients who received inpatient rehabilitation services following a traumatic brain injury (TBI) who discharged AMA and to compare this group with the regular discharge group. RESEARCH DESIGN: Retrospective cohort study. METHODS: Hospital discharge data from two national administrative databases were reviewed for the years 2001-2006. RESULTS: The databases yielded 1559 cases of TBI (average length of stay = 51 days). Of these, 31 (2.0%) had recorded DAMA events: one in 50 patients left rehabilitation against medical advice. Compared to regular discharge (n = 1247), DAMA was significantly associated with unemployment, intentional injury, higher motor functions at admission and shorter length of stay. Known factors for DAMA in acute hospitals, such as male sex, young age and substance abuse history, were not significant. CONCLUSION: Careful screening and assessment of patients who discharge AMA could enable better prevention and management strategies, thus improving health outcomes and enhancing healthcare delivery.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".