Fracture risk among First Nations people: a retrospective matched cohort study
Bibliographic record
Abstract
BACKGROUND: Canadian First Nations people have unique cultural, socioeconomic and health-related factors that may affect fracture rates. We sought to determine the overall and site-specific fracture rates of First Nations people compared with non-First Nations people. METHODS: We studied fracture rates among First Nations people aged 20 years and older (n = 32 692) using the Manitoba administrative health database (1987-1999). We used federal and provincial sources to identify ethnicity, and we randomly matched each First Nations person with 3 people of the same sex and year of birth who did not meet this definition of First Nations ethnicity (n = 98 076). We used a provincial database of hospital separations and physician billing claims to calculate standardized incidence ratios (SIRs) and 95% confidence intervals (CIs) for each fracture type based on a 5-year age strata. RESULTS: First Nations people had significantly higher rates of any fracture (age- and sex-adjusted SIR 2.23, 95% CI 2.18-2.29). Hip fractures (SIR 1.88, 95% CI 1.61-2.14), wrist fractures (SIR 3.01, 95% CI 2.63-3.42) and spine fractures (SIR 1.93, 95% CI 1.79-2.20) occurred predominantly in older people and women. In contrast, craniofacial fractures (SIR 5.07, 95% CI 4.74-5.42) were predominant in men and younger adults. INTERPRETATION: First Nations people are a previously unidentified group at high risk for fracture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".