Descriptive Epidemiology of Fall-Related Injuries Among Older Adults in Ontario, Canada
Bibliographic record
Abstract
Abstract The number of older adults is growing rapidly in the province of Ontario meaning there will be more fall-related injuries (FRIs) in coming decades. Falls are the leading cause of injury-related hospitalizations in Canada. The purpose of this study was to describe the prevalence, circumstances, types, and locations of FRIs among older adults in Ontario. Using a population-based retrospective design, we analyzed secondary data from three health administrative databases (NACRS, DAD, RPDB) for 2010-2014. Older adults (≥ 65 years) admitted to an emergency department (ED) with a combined diagnosis of ICD-10-CA codes for a fall (W00-W19) and injury (S00-S99 or T00-T14) were selected. Descriptive statistics were performed in R and rates were reported per 100,000 population. There were 304,610 FRI ED admissions (3,089/100,000) and 143,210 patients (47.0%) were subsequently hospitalized (1,452/100,000). Females accounted for 63.0% ED and 61.2% hospital admissions. Age-specific rates increased with age at both ED (2,208/100,000 in 65-69 group, 6,552/100,000 in 90+ years old) and hospital (698/100,000 in 65-69 group, 4,364/100,000 in 90+ years old). Females had higher rates of ED (3,503 vs. 2,572/100,000) and hospital (1,598 vs. 1,270/100,000) admissions than males. The most common injury types at the ED were fractures (1,234/100,000), superficial injuries (719/100,000), other or unspecified injuries (572/100,000), open wounds (498/100,000), and sprains, strains, and tears (162/100,000). FRIs are a considerable problem for older adults and better injury prevention strategies are needed for all female age groups, the 90+ year age group of both genders, and fractures.
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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 | 0.003 | 0.008 |
| 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".