Factors Associated With Cardiovascular Mortality After Complicated Mild to Severe Traumatic Brain Injury (TBI): A TBI Model Systems Study
Bibliographic record
Abstract
OBJECTIVE: To characterize factors associated with death due to cardiovascular causes following complicated mild to severe traumatic brain injury (TBI). SETTING: Chart review or telephonic interviews. PARTICIPANTS: Participants enrolled in the TBI Model Systems database. DESIGN: Retrospective. MAIN MEASURES: Primary cause of death due to cardiovascular causes coded with ICD-9 codes 390-459 or ICD-10 codes 100-199 (diseases of the circulatory system) on death certificates. A competing risk cause-specific Cox proportional hazards regression analysis was completed to identify demographic and injury-related factors associated with increased risk of cardiovascular-related mortality. RESULTS: Overall, 15 370 participants were included. Overall, 2,770 (18.0%) individuals died, of which 595 (21.5%) died due to cardiovascular-related causes. Those who died due to cardiovascular causes were older (hazard ratio [HR] 1.08, 95% 1.07-1.09, P <.001), more likely to be male (HR 1.84, 95% CI 1.50-2.26, P <.001), divorced (HR 1.63, 95% CI 1.20-2.23, P = .002), and had lower functional independence measure motor scores (HR 0.99, 95% CI 0.98-0.99, P <.001). Individuals who identified as Asian/Pacific Islander (HR 0.21, 95% CI 0.08-0.55, P = .002), were employed (HR 0.65, 95% CI 0.46-0.90, P = .010), had private insurance (HR 0.77, 95% CI 0.60-0.99, P = .043), and had post-traumatic amnesia (PTA) duration >30 days (HR 0.71, 95% CI 0.55-0.92, P = .010) were less likely to die due to cardiovascular causes. Alcohol or drug use and education level were not significantly associated with death due to cardiovascular causes. CONCLUSION: Over 1 in 5 deaths following TBI were due to cardiovascular causes. Older age, male sex, being divorced, and having lower FIM motor scores are risk factors, whereas being employed, having private health insurance, and PTA >30 days are protective factors for cardiovascular mortality.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".