Temporal Trends in Obesity, Osteoporosis Treatment, Bone Mineral Density, and Fracture Rates: A Population-Based Historical Cohort Study
Bibliographic record
Abstract
Diverging international trends in fracture rates have been observed, with most reports showing that fracture rates have stabilized or decreased in North American and many European populations. We studied two complementary population-based historical cohorts from the Province of Manitoba, Canada (1996-2006) to determine whether declining osteoporotic fracture rates in Canada are attributable to trends in obesity, osteoporosis treatment, or bone mineral density (BMD). The Population Fracture Registry included women aged 50 years and older with major osteoporotic fractures, and was used to assess impact of changes in osteoporosis treatment. The BMD Registry included all women aged 50 years and older undergoing BMD tests, and was used to assess impact of changes in obesity and BMD. Model-based estimates of temporal changes in fracture rates (Fracture Registry) were calculated. Temporal changes in obesity and BMD and their association with fracture rates (BMD Registry) were estimated. In the Fracture Registry (n=27,341), fracture rates declined 1.6% per year (95% confidence interval [CI], 1.3% to 2.0%). Although osteoporosis treatment increased from 5.6% to 17.4%, the decline in fractures was independent of osteoporosis treatment. In the BMD Registry (n=36,587), obesity increased from 12.7% to 27.4%. Femoral neck BMD increased 0.52% per year and lumbar spine BMD increased 0.32% per year after covariate adjustment (p<0.001). Major osteoporotic fracture rates decreased in models that did not include femoral neck BMD (fully adjusted annual change -1.8%; 95% CI, -2.9 to -0.5), but adjusting for femoral neck BMD accounted for the observed reduction (annual change -0.5%; 95% CI, -1.8 to +1.0). In summary, major osteoporotic fracture rates declined substantially and linearly from 1996 to 2006, and this was explained by improvements in BMD rather than greater rates of obesity or osteoporosis treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".