Influence of health insurance on withdrawal of life sustaining treatment for patients with isolated traumatic brain injury: a retrospective multi-center observational cohort study
Bibliographic record
Abstract
BACKGROUND: Healthcare inequities for patients with traumatic brain injury (TBI) represent a major priority area for trauma quality improvement. We hypothesized a relationship between health insurance status and timing of withdrawal of life sustaining treatment (WLST) for adults with severe TBI. METHODS: This multicenter retrospective observational cohort study utilized data collected between 2017 and 2020. We identified adult (age ≥ 16) patients with isolated severe TBI admitted participating Trauma Quality Improvement Program centers. We determined the relationship between insurance status (public, private, and uninsured) and the timing of WLST using a competing risk survival analysis framework adjusting for baseline, clinical, injury and trauma center characteristics. Multivariable cause-specific Cox regressions were used to compute adjusted hazard ratios (HR) reflecting timing of WLST, accounting for mortality events. We also quantified the between-center residual variability in WLST using the median odds ratio (MOR) and measured insurance status association with access to rehabilitation at discharge. RESULTS: We identified 42,111 adults with isolated severe TBI treated across 509 trauma centers across North America. There were 10,771 (25.6%) WLST events in the cohort and a higher unadjusted incidence of WLST events was evident in public insurance patients compared to private or uninsured groups. After adjustment, WLST occurred earlier for publicly insured (HR 1.07, 95% CI 1.02-1.12) and uninsured patients (HR 1.29, 95% CI 1.18-1.41) compared to privately insured patients. Access to rehabilitation was lower for both publicly insured and uninsured patients compared to patients with private insurance. Accounting for case-mix, the MOR was 1.49 (95% CI 1.43-1.55), reflecting significant residual between-center variation in WLST decision-making. CONCLUSIONS: Our findings highlight the presence of disparate WLST practices independently associated with health insurance status. Additionally, these results emphasize between-center variability in WLST, persisting despite adjustments for measurable patient and trauma center characteristics.
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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 | 0.000 | 0.002 |
| 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".