Combined Vertebral Fracture Assessment and Bone Mineral Density Measurement: A Patient-friendly New Tool with an Important Impact on the Canadian Risk Fracture Classification
Bibliographic record
Abstract
PURPOSE: Vertebral fractures often go unnoticed, while they constitute a significant risk factor for new fractures, independent of the bone density. Vertebral Fracture Assessment (VFA) is a new feature on DXA bone densitometry equipment. Our purpose was to determine the added value of VFA and its impact on the Canadian fracture risk classification using data from a Dutch academic cohort. METHODS: All 958 consecutive patients (64% female, mean age 53 [20-94], mean weight 75 kg [32-150]) who underwent BMD measurement at the University Medical Center Groningen, The Netherlands also underwent VFA in the same session. RESULTS: The prevalence of vertebral fractures was 26%. In 68% of these patients this fracture was unknown. The severity was "mild" (20%-25% height loss) in 43%, "moderate" (25%-35%) in 44% and "severe" (>35% height loss) in 13%. Even after excluding mild fractures, the prevalence of vertebral fractures was 17%. In the 28% with normal BMD the vertebral fracture prevalence was still 18%, in the 43% with osteopenia 23%, and in the 29% with osteoporosis 36%. The Canadian risk classification was "low fracture risk" in 68%, "moderate" in 19%, and "high" in 13%. Adding VFA altered the classification in 20% of the patients, to become 54%, 27%, and 19%, respectively. CONCLUSIONS: VFA added to BMD is a patient friendly diagnostic tool with a high diagnostic yield, as it detected unknown vertebral fractures and altered diagnostic classification in approximately 1 out of every 5 patients. These results suggest that BMD plus VFA may become the new standard in osteoporosis testing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".