Recent trends in hospitalization and in-hospital mortality associated with traumatic brain injury in Canada
Why this work is in the frame
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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 2011 using a nationwide, population based database that is mandatory for all hospitals in Canada. METHODS: Trends in hospitalization rates for all acute hospital separations in Canada 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 (age Q65 years). 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, 15Y24 years) were most at risk for MVCs but experienced the greatest decline (28%) in MVC-related admissions. There were significant trends toward increasing age, injury severity, comorbidity, hospital length of stay, and rate of in-hospital mortality.However, multivariate regression showed that odds of death decreased over time after controlling for relevant factors. Injury severity, comorbidity, and advanced age were the most important predictors of in-hospital mortality for TBI inpatients. CONCLUSION: Hospitalizations for TBI are increasing in severity and involve older populations with more complex comorbidities. Although preventive strategies for MVC-related TBI are likely having some effects, there is a critical need for effective fall prevention strategies, especially among elderly adults.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 it