Lifetime costs of traumatic brain injury identified in the emergency department in Ontario
Bibliographic record
Abstract
Background: Traumatic brain injury (TBI) is a leading cause of death and disability, yet there is limited research on its economic burden. We estimated the incidence and lifetime costs of TBI identified in the Emergency Department (ED) in Ontario, Canada between April 2009 and March 2010. Methods: ED visits for TBI were identified using a population-based database that is mandatory for ambulatory care facilities in Ontario. The authors calculated unit costs for medical treatment and productivity loss, and multiplied these by incidence estimates to determine the lifetime costs of identified TBI cases. Results: In 2009, there were over 133,000 ED visits for TBI in Ontario, resulting in a conservative estimate of $945 million in total lifetime costs. Costs were greater for males than females across nearly all age groups, with males incurring two-fold higher costs overall. Together, falls ($407 million), struck by/against ($309 million), and motor vehicle injuries ($161 million) represented 93% of lifetime costs associated with TBI. Conclusions: This study revealed a high incidence and economic burden associated with TBI identified in the ED. More research is needed to fully appreciate the burden of TBI across a variety of healthcare settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".