Recent trends in hospitalization and in-hospital mortality associated with traumatic brain injury in Canada: a nationwide, population-based study
Bibliographic record
Abstract
Background: Traumatic brain injury (TBI) is the leading cause of traumatic death and disability worldwide. We examined nationwide trends in TBI-related hospitalizations and in-hospital mortality between April 2006 and March 2010 using a population-based database that is mandatory for all hospitals in Canada. Methods: Trends in hospitalization rates were analyzed using linear regression. Independent predictors of in-hospital mortality were evaluated using logistic regression. Results: Hospitalization rates remained stable for children and young adults, but increased considerably among elderly adults (ages 65 and older). Falls and motor vehicle collisions (MVCs) were the most common causes of TBI hospitalizations. TBIs caused by falls increased by 24% (p=0.01), while MVC-related hospitalization rates decreased by 18% (p=0.03). Elderly adults were most vulnerable to falls, and experienced the greatest increase (29%) in fall-related hospitalization rates. Young adults (ages 15-24) were most at risk for MVCs, but experienced the greatest decline (28%) in MVC-related admissions. There were significant trends towards increasing age, injury severity, comorbidity, hospital length of stay, and in-hospital mortality. However, multivariate regression showed that the odds of death decreased over time after controlling for relevant factors. Conclusions: Hospitalizations for TBI are increasing in severity and involve older populations with more complex comorbidities.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".