The Importance of Previous Fracture Site on Osteoporosis Diagnosis and Incident Fractures in Women
Bibliographic record
Abstract
Previous fracture increases the risk of subsequent fractures regardless of the site of the initial fracture. Fracture risk assessment tools have been developed to guide clinical management; however, no discrimination is made as to the site of the prior fracture. Our objective was to determine which sites of previous nontraumatic fractures are most strongly associated with a diagnosis of osteoporosis, defined by a bone mineral density (BMD) T-score of ≤ -2.5 at the femoral neck, and an incident major osteoporotic fracture. Using administrative health databases, we conducted a retrospective historical cohort study of 39,991 women age 45 years and older who had BMD testing with dual-energy X-ray absorptiometry (DXA). Logistic regression and Cox proportional multivariate models were used to test the association of previous fracture site with risk of osteoporosis and incident fractures. Clinical fractures at the following sites were strongly and independently associated with higher risk of an osteoporotic femoral neck T-score after adjustment for age: hip (odds ratio [OR], 3.58; 95% confidence interval [CI], 3.04-4.21), pelvis (OR, 2.23; 95% CI, 1.66-3.0), spine (OR, 2.16; 95% CI, 1.77-2.62), and humerus (OR, 1.74; 95% CI, 1.49-2.02). Cox proportional hazards models, with adjustment for age and femoral neck BMD, showed the greatest increase in risk for a major osteoporotic fracture for women who had sustained previous fractures of the spine (hazard ratio [HR], 2.08; 95% CI, 1.72-2.53), humerus (HR, 1.70; 95% CI, 1.44-2.01), patella (HR, 1.54; 95% CI, 1.10-2.18), and pelvis (HR, 1.45; 95% CI, 1.04-2.02). In summary, our results confirm that nontraumatic fractures in women are associated with osteoporosis at the femoral neck and that the site of previous fracture impacts on future osteoporotic fracture risk, independent of BMD.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".