Healthcare Utilization Following Traumatic Brain Injury in a Large National Sample
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of traumatic brain injury (TBI) on healthcare utilization (HCU) over a 1-year period in a large national sample of individuals diagnosed with TBI across multiple care settings. SETTING: Commercial insurance enrollees. PARTICIPANTS: Individuals with and without TBI, 2008-2014. DESIGN: Retrospective cohort. MAIN MEASURES: We compared the change in the 12-month sum of inpatient, outpatient, emergency department (ED), and prescription HCU from pre-TBI to post-TBI to the same change among a non-TBI control group. Most rehabilitation visits were not included. We stratified models by age ≥65 and included the month of TBI in subanalysis. RESULTS: There were 207 354 individuals in the TBI cohort and 414 708 individuals in the non-TBI cohort. Excluding the month of TBI diagnosis, TBI resulted in a slight increase in outpatient visits (rate ratio [RtR] = 1.05; 95% confidence interval [CI], 1.04-1.06) but decrease in inpatient HCU (RtR = 0.86; 95% CI, 0.84-0.88). Including the month of TBI in the models resulted in increased inpatient (RtR = 1.55; 95% CI, 1.52-1.58) and ED HCU (RtR = 1.37; 95% CI, 1.34-1.40). CONCLUSION: In this population of individuals who maintained insurance coverage following TBI, results suggest that TBI may have a limited impact on nonrehabilitation HCU at the population level.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".