Variation in bone mineral density and fractures over 20 years among Canadians: a comparison of the Canadian Multicenter Osteoporosis Study and the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
International variations in osteoporosis and fracture rates have been reported, with temporal trends differing between populations. We observed higher BMD and lower fracture prevalence in a recently recruited cohort compared to that of a cohort recruited 20 years ago, even after adjusting for multiple covariates. PURPOSE: We explored sex-specific differences in femoral neck bone mineral density (FN-BMD) and in prevalent major osteoporotic fractures (MOF) using two Canadian cohorts recruited 20 years apart. METHODS: We included men and women aged 50-85 years from the Canadian Multicentre Osteoporosis Study (CaMos, N = 6,479; 1995-1997) and the Canadian Longitudinal Study on Aging (CLSA, N = 19,534; 2012-2015). We created regression models to compare FN-BMD and fracture risk between cohorts, adjusting for important covariates. Among participants with prevalent MOF, we compared anti-osteoporosis medication use. RESULTS: [- 0.011; 0.000]), while adjusted odds ratios (95%CI) for prevalent MOF were higher in CaMos women (1.99 [1.71; 2.30]) and men (2.33 [1.82; 3.00]) compared to CLSA. In women with prevalent MOF, menopausal hormone therapy use was similar in both cohorts (43.3% vs 37.9%, p = 0.076), but supplements (32.0% vs 48.3%, p < 0.001) and bisphosphonate use (5.8% vs 17.3%, p < 0.001) were lower in CaMos. The proportion of men with MOF who received bisphosphonates was below 10% in both cohorts. CONCLUSION: Higher BMD and lower fracture prevalence were noted in the more recently recruited CLSA cohort compared to CaMos, even after adjusting for multiple covariates. We noted an increase in bisphosphonate use in the recent cohort, but it remained very low in men.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".