Effect of Holding Objects on the Occurrence of Head Impact in Falls by Older Adults: Evidence From Real-Life Falls in Long-Term Care
Bibliographic record
Abstract
BACKGROUND: Falls cause approximately 80% of traumatic brain injuries in older adults, and nearly one third of falls by residents in long-term care (LTC) result in head impact. Holding objects during falls, such as mobility aids, may affect the ability of LTC residents to avoid head impact by arresting the fall with their upper limbs. We examined the prevalence of holding objects and their effect on risk for head impact during real-life falls in older adults living in LTC. METHODS: We analyzed videos of 1105 real-life falls from standing height by 425 LTC residents, using a validated questionnaire to characterize the occurrence of head impact and whether the resident held objects during descent and impact. We classified objects as either "weight-bearing" (via contact to the fixed environment, eg, chairs and walkers) or "non-weight-bearing" (eg, cups) and tested their effect on odds for head impact with generalized estimating equations. RESULTS: Residents held objects in more than 60% of falls. The odds for head impact were reduced for falls where weight-bearing objects were held or grasped during descent (odds ratio = 0.52; 95% confidence interval = 0.39-0.70) or maintained throughout the fall (odds ratio = 0.34; 95% confidence interval = 0.23-0.49). The most commonly held objects were chairs/wheelchairs (23% of cases), tables/counters (10% of cases), and walkers/rollators (22% of cases); all reduced the odds of head impact when held during descent. Holding non-weight-bearing objects did not affect the odds of head impact (odds ratio = 1.00; 95% confidence interval = 0.64-1.55). CONCLUSION: Our results show that older adults in LTC use held, weight-bearing objects to reduce their risk for head impact during falls.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".