Protective responses of older adults for avoiding injury during falls: evidence from video capture of real-life falls in long-term care
Bibliographic record
Abstract
BACKGROUND: falls are common in older adults, and any fall from standing height onto a rigid surface has the potential to cause a serious brain injury or bone fracture. Safe strategies for falling in humans have traditionally been difficult to study. OBJECTIVE: to determine whether specific 'safe landing' strategies (body rotation during descent, and upper limb bracing) separate injurious and non-injurious falls in seniors. DESIGN: observational cohort study. SETTING: two long-term care homes in Vancouver BC. METHODS: videos of 2,388 falls experienced by 658 participants (mean age 84.0 years; SD 8.1) were analysed with a structured questionnaire. General estimating equations were used to examine how safe landing strategies associated with documented injuries. RESULTS: injuries occurred in 38% of falls, and 4% of falls caused injuries treated in hospitals. 32% of injuries were to the head. Rotation during descent was common and protective against injury. In 43% of falls initially directed forward, participants rotated to land sideways, which reduced their odds for head injury 2-fold. Upper limb bracing was used in 58% of falls, but rather than protective, bracing was associated with an increased odds for injury, possibly because it occurred more often in the demanding scenario of forward landings. CONCLUSIONS: the risk for injury during falls in long-term care was reduced by rotation during descent, but not by upper limb bracing. Our results expand our understanding of human postural responses to falls, and point towards novel strategies to prevent fall-related injuries.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".