Predictors of falls and mortality among elderly adults with traumatic brain injury: A nationwide, population-based study
Bibliographic record
Abstract
BACKGROUND: Elderly adults are at particular risk of sustaining a traumatic brain injury (TBI), and tend to suffer worse outcomes compared to other age groups. Falls are the leading cause of TBI among the elderly. METHODS: We examined nationwide trends in TBI hospitalizations among elderly adults (ages 65 and older) between April 2006 and March 2011 using a population-based database that is mandatory for all hospitals in Canada. Trends in admission rates were analyzed using linear regression. Predictors of falls and in-hospital mortality were identified using logistic regression. RESULTS: Between 2006 and 2011, there were 43,823 TBI hospitalizations resulting in 6,939 deaths among elderly adults in Canada. Over the five-year study period, the overall rate of TBI admissions increased by an average of 6% per year from 173.2 to 214.7 per 100,000, while the rate of fall-related TBI increased by 7% annually from 138.6 to 179.2 per 100,000. There were significant trends towards increasing age and comorbidity level (p<0.001 and p = 0.002). Advanced age, comorbidity, and injury severity were independent predictors of both TBI-related falls and mortality on multivariate analysis. CONCLUSION: Prevention efforts should be targeted towards vulnerable demographics including the "older old" (ages 85 and older) and those with multiple medical comorbidities. Additionally, hospitals and long-term care facilities should be prepared to manage the burgeoning population of older patients with more complex comorbidities.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".