Discharge Against Medical Advice After Traumatic Brain Injury: Is Intentional Injury a Predictor?
Bibliographic record
Abstract
BACKGROUND: Discharge against medical advice (DAMA) have consistently been reported as causing adverse outcomes for both patients and service providers. However, little is known about the DAMA of patients with traumatic brain injury (TBI). The objectives of this study were to develop a risk profile of DAMA patients in the TBI population, to examine factors associated with DAMA occurrence, and to examine specifically whether injury intention (unintentional vs. intentional) is a significant predictor of DAMA. METHODS: A retrospective cohort study was conducted using hospital discharge data obtained from the Minimal Data Set (MDS) of the Ontario Trauma Registry for the years 1993/1994 and 2000/2001 on TBI patients aged 15 to 64 years. RESULTS: The MDS review yielded 15,684 cases of TBI with an average length of stay of 2.7 days. Of these, 446 (2.84%) had recorded DAMA events. When compared with patients with unintentional TBI, DAMA was significantly associated with intentional injuries in those with self-inflicted TBI (adjusted odds ratio [aOR] = 1.97; 95% confidence interval [CI], 1.36-2.84) and other-inflicted TBI (aOR = 2.00; CI, 1.53-2.62). DAMA was also associated with younger age and a history of alcohol/drug abuse (aOR = 3.50; CI, 2.85-4.30). CONCLUSION: TBI patients who leave hospital against medical advice are a high-risk population. Early identification of these patients could allow implementation of 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".