Who Is Most At Risk Of Suffering An Injurious Fall On Snow Or Ice?
Bibliographic record
Abstract
Falls are a leading cause of injury, hospitalization, and death among older adults, costing Canada approximately $5.6 million annually. Many factors contribute to fall risk, and despite the popular belief that adverse weather impacts the risk of falls, this association is largely under-studied. PURPOSE: To determine which individual characteristics are associated with an increased risk of sustaining an injurious fall on snow or ice in the Canadian Study on Longitudinal Aging cohort. METHODS: Data from 51,338 participants in the Canadian Longitudinal Study on Aging (aged 45-85 at baseline) were used to build a profile (e.g., age, sex, body mass index, chronic conditions, medications, body composition, physical function variables) of those who are most at risk of suffering an injurious fall on snow or ice. To do so, participants were compared by being grouped into one of three categories: 1 - did not suffer an injurious fall, 2 - suffered an injurious fall on snow or ice, or 3 - suffered an injurious fall elsewhere. RESULTS: A total of 254 individuals reported an injurious fall on snow or ice (average age: 62.1 ± 9.8 years; 50.8% male), and 2,342 reported an injurious fall elsewhere (average age: 63.9 ± 10.5 years; 39.2% male). No significant differences were found between those who did not suffer an injurious fall and those who suffered an injurious fall on snow or ice. Compared to those who suffered an injurious fall elsewhere, those who suffered an injurious fall on snow or ice were significantly younger (p = .006), more likely to be male (p < .001) and had a significantly stronger grip strength (p = .005). CONCLUSION: These results suggest that being a middle-aged man is associated with a higher risk of an injurious fall on snow or ice. Longitudinal data will be used to confirm this association. This work could lead to targeted public health guidelines to reduce the risk of injurious falls on snow/ice.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".