BONE MICROARCHITECTURE DECLINE AND RISK OF FALL AND FRACTURE IN MEN WITH POOR PHYSICAL PERFORMANCE – THE STRAMBO STUDY
Bibliographic record
Abstract
Context: High fracture risk in subjects with low muscle strength is attributed to high risk of fall. Objective: To study the association of muscle mass and physical performance with bone microarchitecture decline and risk of fall and nonvertebral fracture in men. Design: Prospective 8-year follow-up of a cohort. Setting: General population. Participants: 821 volunteer men aged ≥60. Interventions: Hip areal bone mineral density (aBMD) and appendicular lean mass (ALM) were assessed at baseline by DXA. Lower limb relative ALM (RALM-LL) is ALM-LL/(leg length)2. The physical performance score reflects ability to perform chair stands and static and dynamic balance. Bone microarchitecture was assessed by high resolution peripheral QCT (HR-pQCT) at baseline, after 4 and 8 years. Statistical analyses were adjusted for shared risk factors. Outcomes: Rate of change in the HR-pQCT indices, incident falls and fractures. Results: Cortical bone loss and estimated bone strength decline were faster in men with low vs. normal RALM-LL (failure load: -0.74±0.09 vs. -0.43±0.10%/year; p<0.005). Differences were similar between men with poor and those with normal physical performance (failure load: -1.12 ±0.09 vs. -0.40±0.05%/year; p<0.001). Differences were similar between men having poor performance and low RALM-LL and men having normal RALM-LL and performance (failure load: -1.40±0.17 vs. -0.47±0.03%/year; p<0.001). Men with poor physical performance had higher risk of fall (HR=3.52, 95%CI: 1.57–7.90, p<0.05) and fracture (HR=2.68, 95%CI: 1.08–6.66, p<0.05). Conclusion: Rapid decline of bone microarchitecture and estimated strength in men with poor physical performance and low RALM-LL may contribute to higher fracture risk.
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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) | 0.001 | 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 | 0.001 |
| 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".