Time since prior fracture is a risk modifier for 10-year osteoporotic fractures
Bibliographic record
Abstract
The objective of this study was to assess the importance of time since prior fracture as a risk factor for future osteoporotic fractures and how it affects 10-year fracture rates. We identified 39,991 women 45 years of age or older undergoing baseline bone mineral density (BMD) testing (1990-2007) from a regional database that contains dual-energy X-ray absorptiometry (DXA) results for Manitoba, Canada. Health service records were used to identify nontrauma ICD-9-CM fracture codes preceding DXA, grouped as "major" fractures (n = 5178; hip, spine, forearm, and humerus) or "minor" fractures (n = 3479; ribs, sternum, pelvis, trunk, clavicle, scapula, patella, tibia/fibula, and ankle). Time since prior fracture was coded in years as less than 1, 1 to 5, 5 to 10, and more than 10. Incident fractures (ie, hip, spine, forearm, and humerus) after BMD testing were identified (mean follow-up 4.2 years, maximum 10 years) and studied in Cox proportional-hazards models adjusted for age, BMD T-score, and other covariates. After BMD testing, n = 1749 (4.4%) women experienced an incident fracture. Prior major fracture was a strong risk factor for incident fracture, greatest risk in the first year [hazard ratio (HR) 1.90, 95% confidence interval (CI) 1.60-2.25], declining by more than 10 years (HR 1.62, 95% CI 1.25-2.10). Prior minor fracture was a weaker risk factor, greatest in the first year (HR 1.45, 95% CI 1.13-1.87) and no longer significant by 1 to 5 years. Major and minor fractures both showed a time-dependent decline in importance as risk factors. In conclusion, time since prior fracture modifies future fracture risk, but prior fractures of the hip, spine, forearm, and humerus remain strong risk factors even 10 years later. Fracture risk assessment should emphasize the importance of prior fractures at these sites.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".