Effectiveness of Bone Density Measurement for Predicting Osteoporotic Fractures in Clinical Practice
Bibliographic record
Abstract
CONTEXT: Bone density measurement with dual-energy x-ray absorptiometry is widely used for fracture risk assessment. It has not been established that published gradients of fracture risk from study populations can be directly applied to clinical populations. OBJECTIVE: The objective of the study was to assess osteoporotic fracture prediction with dual-energy x-ray absorptiometry in a large clinical cohort. DESIGN: This was a historical cohort study (mean observation period 3.2 +/- 1.5 yr). PATIENTS: The study population was drawn from the population-based database of the Manitoba Bone Density Program. Analyses were limited to women aged 50 yr or older at baseline (n = 16,505). MAIN OUTCOME MEASURE: Each subject's longitudinal health service record was assessed for the presence of nontrauma fracture codes (hip, spine, wrist, and humerus) after bone density testing. Age-adjusted hazard ratios for fracture were derived from Cox proportional hazards models. RESULTS: Site-specific and overall fracture rates were significantly associated with each site of bone density measurement (all P < 0.00001). The 95% confidence intervals overlapped those from a widely cited metaanalysis of fracture prediction from different sites. Although fracture prediction was not significantly different between the three hip measurement sites, each hip site was better than the lumbar spine for predicting overall fractures (nonoverlapping 95% confidence intervals). The manufacturer sd (equivalent to a unit change in T-score) resulted in a significantly smaller gradient of risk for the spine than when the population sd was used. CONCLUSIONS: Bone density measurements are effective for predicting fractures in clinical practice. However, hip measurements were superior to the spine in overall osteoporotic fracture prediction.
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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 | 0.040 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".