Association Between Change in BMD and Fragility Fracture in Women and Men
Bibliographic record
Abstract
Our objective was to estimate the relationship between longitudinal change in BMD and fragility fractures. We studied 3635 women and 1417 men 50-85 yr of age in the Canadian Multicentre Osteoporosis Study who had at least two BMD measurements (lumbar spine, femoral neck, total hip, and trochanter) within the first 5 yr of the study and fragility fractures (any, main, forearm/wrist, ribs, hip) within the first 7 yr. Multiple logistic regression was used to model the relationship between baseline BMD, BMD change, and fragility fractures. We found that, among nonusers of antiresorptives, independent of baseline BMD, a decrease of 0.01 g/cm(2)/yr in total hip BMD was associated with an increased risk of fragility fracture with ORs of 1.15 (95% CI: 1.01; 1.32) in women and 1.34 (95% CI: 1.02; 1.78) in men. The risk of fragility fractures in subgroups such as fast losers and those with osteopenia was better estimated by models that included BMD change than by models that included baseline BMD but excluded BMD change. Although the association between baseline BMD and fragility fractures was similar in users and nonusers of antiresorptives, the association was stronger in nonusers compared with users. These results show that BMD change in both men and women is an independent risk factor for fragility fractures and also predicts fracture risk in those with osteopenia. The results suggest that BMD change should be included with other variables in a comprehensive fracture prediction model to capture its contribution to osteoporotic fracture risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".