Clinical Risk Factors for Head Impact During Falls in Older Adults: A Prospective Cohort Study in Long-Term Care
Bibliographic record
Abstract
OBJECTIVE: To examine risk factors associated with head impact during falls in older adults in long-term care (LTC). SETTING: Two LTC facilities in British Columbia, Canada. PARTICIPANTS: 160 LTC residents. DESIGN: Prospective cohort study. MAIN MEASURES: Between 2007 and 2014, we video captured 520 falls experienced by participants. Each fall video was analyzed to determine whether impact occurred to the head. Using generalized estimating equation models, we examined how head impact was associated with other fall characteristics and health status prior to the fall. RESULTS: Head impact occurred in 33% of falls. Individuals with mild cognitive impairment were at higher risk for head impact (odds ratio = 2.8; 95% confidence interval, 1.5-5.0) than those with more severe cognitive impairment. Impaired vision was associated with 2.0-fold (1.3-3.0) higher odds of head impact. Women were 2.2 times (1.4-3.3) more likely than men to impact their head during a fall. CONCLUSION: Head impact is common during falls in LTC, with less cognitively impaired, female residents who suffered from visual impairment, being most likely to impact their head. Future research should focus on improving our ability to detect neural consequences of head impact and evaluating the effect of interventions for reducing the risk for fall-related head injuries in LTC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".