Long-Term and Recent Weight Change Are Associated With Reduced Peripheral Bone Density, Deficits in Bone Microarchitecture, and Decreased Bone Strength: The Framingham Osteoporosis Study
Bibliographic record
Abstract
Weight loss in older adults is associated with increased bone loss and fracture. Little is known about the potential impact of weight loss on cortical and trabecular bone density, microarchitecture, and strength. In this study, participants were members of the Framingham Offspring Cohort (769 women, 595 men; mean age 70 ± 8 years), who underwent high-resolution peripheral quantitative computed tomography (HR-pQCT) scanning at the tibia and radius in 2012 to 2016. Weight measurements taken every 4 to 6 years were used to assess recent weight change over 6 years and long-term change over 40 years. General linear models, adjusting for age, sex, height, smoking, and diabetes, were used to evaluate the association between HR-pQCT indices and relative long-term and recent weight change. We found that long-term and recent weight loss were associated with lower cortical density and thickness, higher cortical porosity, and lower trabecular density and number. Associations were stronger for the tibia than radius. Failure load was lower in those individuals with long-term but not short-term weight loss. Deterioration in both cortical and trabecular indices, especially at the weight-bearing skeleton, characterizes bone fragility associated with long-term and recent weight loss in older adults. © 2018 American Society for Bone and Mineral Research.
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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 | 0.002 |
| 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 | 0.001 |
| 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".