Bone-muscle indices as risk factors for fractures in men: the Osteoporotic Fractures in Men (MrOS) Study.
Bibliographic record
Abstract
OBJECTIVE: To assess bone-muscle (B-M) indices as risk factors for incident fractures in men. METHODS: Participants of the Osteoporotic Fractures in Men (MrOS) Study completed a peripheral quantitative computed tomography scan at 66% of their tibial length. Bone macrostructure, estimates of bone strength, and muscle area were computed. Areal bone mineral density (aBMD) and body composition were assessed with dual-energy X-ray absorptiometry. Four year incident non-spine and clinical vertebral fractures were ascertained. B-M indices were expressed as bone-to-muscle ratios for: strength, mass and area. Discriminative power and hazards ratios (HR) for fractures were reported. RESULTS: In 1163 men (age: 77.2±5.2 years, body mass index (BMI): 28.0±4.0 kg/m(2), 4.1±0.9 follow-up years, 7.7% of men ⋝1 fracture), B-M indices were smaller in fractured men except for bending and areal indices. Smaller B-M indices were associated with increased fracture risk (HR: 1.30 to 1.74) independent of age and BMI. Strength and mass indices remained significant after accounting for lumbar spine but not total hip aBMD. However, aBMD correlated significantly with B-M indices. CONCLUSION: Mass and bending B-M indices are risk factors for fractures in men, but may not improve fracture risk prediction beyond that provided by total hip aBMD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".