Health & Economic Burden of Traumatic Brain Injury in the Emergency Department
Bibliographic record
Abstract
OBJECTIVE: To evaluate epidemiological patterns and lifetime costs of traumatic brain injury (TBI) identified in the emergency department (ED) within a publicly insured population in Ontario, Canada, in 2009. METHODS: A nationally representative, population-based database was used to identify TBI cases presenting to Ontario EDs between April 2009 and March 2010. We calculated unit costs for medical treatment and productivity loss, and multiplied these by corresponding incidence estimates to determine the lifetime costs of identified TBI cases across age group, sex, and mechanism of injury. RESULTS: In 2009, there were more than 133,000 ED visits for TBI in Ontario, resulting in a conservative estimate of $945 million in lifetime costs. Lifetime cost estimates ranged from $279 million to $1.22 billion depending on the diagnostic criteria used to define TBI. Peak rates of TBI occurred among young children (ages 0-4 year) and the elderly (ages 85+ years). Males experienced a 53% greater rate of TBI and incurred two-fold higher costs compared with females. Falls, sports/bicyclist-related injuries, and motor vehicle crashes represented 47%, 12%, and 10% of TBI presenting to ED, respectively, and accounted for a significant proportion of costs. CONCLUSIONS: This study revealed an enormous health and economic burden associated with TBI identified in the ED setting. Our findings underscore the importance of ongoing surveillance and prevention efforts targeted to vulnerable populations. More research is needed to fully appreciate the burden of TBI across a variety of health care settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".