Association of age with death and withdrawal of life-sustaining therapy after severe traumatic brain injury
Bibliographic record
Abstract
BACKGROUND: Compared to younger age, older age (≥ 65 yr) is associated with worse outcomes after severe traumatic brain injury (TBI). We sought to describe the association of older age with in-hospital death and aggressiveness of intervention. METHODS: We conducted a retrospective cohort study of adult (age ≥ 16 yr) patients with severe TBI admitted to a single academic tertiary care neurotrauma centre between January 2014 and December 2015. We collected data through chart review as well as from our institutional administrative database. We provided descriptive statistics and used multivariable logistic regression to evaluate the independent association of age with the primary outcome, in-hospital death. The secondary outcome was early withdrawal of life-sustaining therapy. RESULTS: There were 126 adult patients (median age 67 yr [Q1-Q3, 33-80 yr]) with severe TBI during the study period who met our eligibility criteria. The most common mechanism was high-velocity blunt injury (55 patients [43.6%]). The median Marshall score was 4 (Q1-Q3, 2-6), and the median Injury Severity Score was 26 (Q1-Q3, 25-35). After controlling for confounders including clinical frailty, pre-existing comorbidity, injury severity, Marshall score and neurologic examination at admission, we observed that older patients were more likely than younger patients to die in hospital (odds ratio 5.10, 95% confidence interval 1.65-15.78). Older patients were also more likely to experience early withdrawal of life-sustaining therapy and less likely to receive invasive interventions. CONCLUSION: After controlling for confounding factors relevant to older patients, we observed that age was an important and independent predictor of in-hospital death and early withdrawal of life-sustaining therapy. The mechanism by which age influences clinical decision-making independent of global and neurologic injury severity, clinical frailty and comorbidities remains unclear.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".