Development of Models for Assessing First Incident Fragility Fracture Risk in Postmenopausal Women: Data from the CLSA
Bibliographic record
Abstract
Objective: To develop models for assessing first incident fragility fracture risk in postmenopausal women which could be used as a screening tool in primary care and similar settings. Design: Cohort study. Methods: Outcome was defined as first incident fragility fracture reported at either year 3 or year 6. Model development was conducted using logistic regression with multiple imputation as sensitivity analysis. Model performance was assessed through AUC, sensitivity, and specificity. Results: Analysis included 10930 female participants (1048 events) aged 45 to 85 years old without a history of fragility fracture before baseline from the Canadian Longitudinal Study on Aging (CLSA). The final model consisted of 11 factors including age, alcohol consumption, antidepressants, balance, epilepsy, hand grip strength, height, osteoarthritis, parent hip fracture after age 50, fall in the past 12 months, and smoking. The model outperformed BMD T-score total hip alone showing moderate discrimination with an AUC of 0.63 [0.61, 0.65]. With a threshold of a fracture probability at 7.30%, sensitivity was 80.49% and specificity was 34.61%. After adjusting for BMD T-score total hip, antidepressants, balance, epilepsy, hand grip strength, height, osteoarthritis, parent hip fracture after age 50, and fall in the past 12 months remained statistically significant. Conclusion: This model uses routinely collected factors and shows reasonable ability to distinguish between individuals at higher and lower risk of first incident fragility fracture. It demonstrates good sensitivity capturing the majority of true cases. Although its specificity is relatively limited, the model still has potential as a screening tool to help identify those at high risk who might benefit from further examinations and early intervention. Further studies are needed to validate the model performance in external populations
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".