Improved fracture risk prediction by adding VFA-identified vertebral fracture data to BMD by DXA and clinical risk factors used in FRAX
Bibliographic record
Abstract
Vertebral fracture (VF) is a strong predictor of subsequent fracture. In this study of older women, VF, identified by dual-energy X-ray absorptiometry (DXA) vertebral fracture assessment (VFA), were associated with an increased risk of incident fractures and had a substantial impact on fracture probability, supporting the utility of VFA in clinical practice. PURPOSE: Clinical and occult VF can be identified using VFA with dual-energy X-ray absorptiometry (DXA). The aim of this study was to investigate to what extent VFA-identified VF improve fracture risk prediction, independently of bone mineral density (BMD) and clinical risk factors used in FRAX. METHODS: A total of 2852 women, 75-80 years old, from the prospective population-based study SUPERB cohort, were included in this study. At baseline, BMD was measured by DXA, VF diagnosed by VFA, and questionnaires used to collect data on risk factors for fractures. Incident fractures were captured by X-ray records or by diagnosis codes. An extension of Poisson regression was used to estimate the association between VFA-identified VF and the risk of fracture and the 5- and 10-year probability of major osteoporotic fracture (MOF) was calculated from the hazard functions for fracture and death. RESULTS: During a median follow-up of 5.15 years (IQR 4.3-5.9 years), the number of women who died or suffered a MOF, clinical VF, or hip fracture was 229, 422, 160, and 124, respectively. A VFA-identified VF was associated with an increased risk of incident MOF (hazard ratio [HR] = 1.78; 95% confidence interval [CI] 1.46-2.18), clinical VF (HR = 2.88; 95% [CI] 2.11-3.93), and hip fracture (HR = 1.67; 95% [CI] 1.15-2.42), adjusted for age, height, and weight. For women at age 75 years, a VFA-identified VF was associated with 1.2-1.4-fold greater 10-year MOF probability compared with not taking VFA into account, depending on BMD. CONCLUSION: Identifying an occult VF using VFA has a substantial impact on fracture probability, indicating that VFA is an efficient method to improve fracture prediction in older women.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".