Discharge Against Medical Advice in Traumatic Brain Injury: Follow-Up and Readmission Rate
Bibliographic record
Abstract
BACKGROUND: Patients who leave hospital against medical advice (AMA) may be at risk of adverse health outcomes, medical complications, and readmission. In this study, we examined the characteristics of patients who left AMA after traumatic brain injury (TBI), their rates of follow-up visits, and readmission. METHODS: We retrospectively studied 106 consecutive patients who left the tertiary trauma center AMA (1.8% of all admitted patients with a TBI). Preinjury health and social issues, mechanism of injury, computed tomography findings, and injury markers were collected. They were correlated to compliance with follow-up visits and unplanned emergency room (ER) visits and readmission rates. RESULTS: The most prevalent premorbid health or social-related issues were alcohol abuse (33%) and assault as a mechanism of trauma (33%). Only 15 (14.2%) subjects came to follow-up visit for their TBI. Sixteen (15.1%) of the 106 subjects had multiple readmissions and/or ER visits related to substance abuse. Seven (6.6%) had multiple readmissions or ER visits with psychiatric reasons. Those patients with multiple readmissions and ER visits showed in higher proportion preexisting neurological condition (p=0.027), homelessness (p=0.012), previous neurosurgery (p=0.014), preexisting encephalomalacia (p=0.011), and had a higher ISS score (p=0.014) than those who were not readmitted multiple times. CONCLUSIONS: The significantly increased risks of multiple follow-up visits and readmission among TBI patients who leave hospital AMA are related to a premorbid vulnerability and psychosocial issues. Clinicians should target AMA TBI patients with premorbid vulnerability for discharge transition interventions.
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.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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; both teacher heads agree on what is shown here.
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".