Epidemiological patterns of traumatic brain injury identified in the emergency department in Ontario, 2002-2010
Bibliographic record
Abstract
Background: Traumatic brain injury (TBI) is the leading cause of traumatic death and disability, and most TBIs are treated in the Emergency Department (ED). We examined the incidence and epidemiological patterns of TBIs presenting to Ontario EDs over an eight-year period. Methods: All TBI-related ED visits between April 2002 and March 2010 were identified using a population-based database that is mandatory for ambulatory care facilities in Ontario. Incidence rates were reported across multiple strata, including age group, sex, and mechanism of injury. Results: From 2002-2010, there were 1,032,249 ED visits for TBI in Ontario. Peak rates occurred among young children ages 0-4 (349 per 10,000) and elderly adults ages 85+ (243 per 10,000). Overall, males experienced a 53% greater rate of TBI compared to females. Falls (47%), motor vehicle crashes (MVC; 10%), and sports-related injuries (9%) were the most common causes of TBI. The highest rates of TBI-related falls, MVCs, and sports-related injuries occurred among young children (0-4) and elderly adults (85+), adolescents/young adults (15-24), and children (5-14), respectively. Conclusions: Our study reveals a substantial health system burden associated with TBI in the ED setting, underscoring the need for enhanced surveillance and prevention efforts targeted to vulnerable demographic groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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".