Bone strength measured by peripheral quantitative computed tomography and the risk of nonvertebral fractures: The osteoporotic fractures in men (MrOS) study
Bibliographic record
Abstract
Many fractures occur in individuals without osteoporosis defined by areal bone mineral density (aBMD). Inclusion of other aspects of skeletal strength may be useful in identifying at-risk subjects. We used surrogate measures of bone strength at the radius and tibia measured by peripheral quantitative computed tomography (pQCT) to evaluate their relationships with nonvertebral fracture risk. Femoral neck (FN) aBMD, measured by dual-energy X-ray absorptiometry (DXA), also was included. The study population consisted of 1143 white men aged 69+ years with pQCT measures at the radius and tibia from the Minneapolis and Pittsburgh centers of the Osteoporotic Fractures in Men (MrOS) study. Principal-components analysis and Cox proportional-hazards modeling were used to identify 21 of 58 pQCT variables with a major contribution to nonvertebral incident fractures. After a mean 2.9 years of follow-up, 39 fractures occurred. Men without incident fractures had significantly greater bone mineral content, cross-sectional area, and indices of bone strength than those with fractures by pQCT. Every SD decrease in the 18 of 21 pQCT parameters was significantly associated with increased fracture risk (hazard ration ranged from 1.4 to 2.2) independent of age, study site, body mass index (BMI), and FN aBMD. Using area under the receiver operation characteristics curve (AUC), the combination of FN aBMD and three radius strength parameters individually increased fracture prediction over FN aBMD alone (AUC increased from 0.73 to 0.80). Peripheral bone strength measures are associated with fracture risk and may improve our ability to identify older men at high risk of fracture.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".