Hip Fractures in Older Adults in Ontario, Canada—Monthly Variation, Insights, and Implications
Bibliographic record
Abstract
BACKGROUND: In older adults, hip fractures have been described to peak in cooler months. Seasonal differences in patient vulnerability to fracture and social/behavioural factors might contribute to these trends. METHODS: Using linked health-care databases in Ontario Canada, we examined monthly variation in hip fracture hospitalizations in those > 65 years (2011-2015). We stratified results by age category (66-79, ≥80 years). We then examined for variation in the demographic and comorbidity profiles of patients across the months, and as an index of contributing social/behavioural factors, noted variation in health-care behaviours. RESULTS: There were 47,971 and 52,088 hospitalizations for hip fracture in those 66-79, and ≥80 years, respectively. There was strong seasonality in fractures in both groups. Peaks occurred in October and December when patients appeared most vulnerable. Rates fell in the summer in those 66-79 years, and in the late winter in those ≥80 years (when health-care utilization also declined). A smaller peak in fractures occurred in May in both groups. CONCLUSIONS: Hip fractures peak in the autumn, early winter, and spring in Canada. A dip in fractures occurs in the late winter in the oldest old. Environmental factors might play a role, but seasonal vulnerability to fracture and winter isolation might also be influential.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".