A population-based study of fall-related traumatic brain injury identified in older adults in hospital emergency departments
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine the population-based trends and factors associated with hospitalization of patients with traumatic brain injury (TBI) treated in the Emergency Department (ED) among those 65 years and older. The implications of these trends for neurosurgery and the broader society are discussed. METHOD: With a national, mandatory reporting system of ED visits, the authors used Poisson regression controlling for age and sex to analyze trends in fall-related TBI of those aged 65 years and older between 2002 and 2017. RESULTS: The overall rate of ED visits for TBI increased by 78%-from 689.51 per 100,000 (95% CI 676.5-702.8) to 1229 per 100,000 (95% CI 1215-1243) between 2002 and 2017. Females consistently experienced higher rates of fall-related TBI than did males. All age groups 65 years and older experienced significant increases in fall-related TBI rate over the study period; however, the highest rates occurred among the oldest individuals (90+ and 85-89 years). The hospital admission rate increased with age and Charlson Comorbidity Index. Males experienced both a higher admission rate and a greater percentage change in admission rate than females. CONCLUSIONS: Rates of ED visits for fall-related TBI, hospitalization, and in-ED mortality in those aged 65 years and older are increasing for both sexes. The increasing hospital admission rate is related to more advanced comorbidities, male sex, and increasing age. These findings have significant implications for neurosurgical resources; they emphasize that health professionals should work proactively with patients, families, and caregivers to clarify goals of care, and they also outline the need for more high-level and, preferably, randomized evidence to support outcomes-based decisions. Additionally, the findings highlight the urgent need for improved population-based measures for prevention in not only this age demographic but in younger ones, and the need for changes in the planning of health service delivery and long-term care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".