Previous fractures at multiple sites increase the risk for subsequent fractures: The global longitudinal study of osteoporosis in women
Bibliographic record
Abstract
Previous fractures of the hip, spine, or wrist are well-recognized predictors of future fracture, but the role of other fracture sites is less clear. We sought to assess the relationship between prior fracture at 10 skeletal locations and incident fracture. The Global Longitudinal Study of Osteoporosis in Women (GLOW) is an observational cohort study being conducted in 17 physician practices in 10 countries. Women aged ≥55 years answered questionnaires at baseline and at 1 and/or 2 years (fractures in previous year). Of 60,393 women enrolled, follow-up data were available for 51,762. Of these, 17.6%, 4.0%, and 1.6% had suffered 1, 2, or ≥3 fractures, respectively, since age 45 years. During the first 2 years of follow-up, 3149 women suffered 3683 incident fractures. Compared with women with no previous fractures, women with 1, 2, or ≥3 prior fractures were 1.8-, 3.0-, and 4.8-fold more likely to have any incident fracture; those with ≥3 prior fractures were 9.1-fold more likely to sustain a new vertebral fracture. Nine of 10 prior fracture locations were associated with an incident fracture. The strongest predictors of incident spine and hip fractures were prior spine fracture (hazard ratio [HR] = 7.3) and hip (HR = 3.5). Prior rib fractures were associated with a 2.3-fold risk of subsequent vertebral fracture, and previous upper leg fracture predicted a 2.2-fold increased risk of hip fracture. Women with a history of ankle fracture were at 1.8-fold risk of future fracture of a weight-bearing bone. Our findings suggest that a broad range of prior fracture sites are associated with an increased risk of incident fractures, with important implications for clinical assessments and risk model development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".