Characteristics of patients with acute traumatic brain injury discharged against medical advice in a Level 1 urban trauma centre
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To predict which characteristics are associated with patients at risk of discharge against medical advice (AMA). RESEARCH DESIGN: Data were retrospectively collected on individuals (n = 5642) admitted to the Traumatic Brain Injury Program of the MUHC-MGH. METHODS AND PROCEDURES: Outcome measures used were length of stay (LOS), the Extended Glasgow Outcome Scale (GOSE) as well as the Functional Independence Measure (FIM®). MAIN OUTCOMES: The overall rate of patients leaving AMA was 1.9% (n = 108). Age was negatively associated with AMA discharge (95% CI OR = [0.966;0.991]). Patients with a history of substance abuse were ∼2-times more likely to leave AMA than those not using substances before injury (95% CI OR = [1.172;3.314]) and the homeless were ∼3-times more likely to leave AMA compared to those who were not homeless (95% CI OR = [1.260;7.138]). Length of stay (LOS) was shorter for patients leaving AMA (p < 0.001) and they showed better outcome (GOSE: p < 0.001; FIM: p = 0.032). CONCLUSIONS: Knowing the profile of patients with TBI leaving AMA hospitalized in an urban Level 1 Trauma centre will help in the development of effective strategies based on patient needs, values and pre-injury psychosocial situation to encourage them to complete their treatment course in hospital.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".