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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".