Frailty and osteoporotic fractures represent mutual risks for each other with common physiological backgrounds
Bibliographic record
Abstract
Abstract Frailty and osteoporosis are known to exacerbate each other. However, limited research is available on the shared pathophysiological factors contributing to osteoporotic fractures and frailty. This study aims to identify common factors associated with both the current frailty and the occurrence of incident vertebral fractures. A total of 912 postmenopausal Japanese women, 63.9 ± 10.0 yr of age (mean ± SD), were included in this study. Each participant’s baseline frailty status was assessed using a questionnaire about the following 5 items: fatigue, resistance, ambulation, inactivity, and weight loss. A score of 3 or above indicated the prevalence of frailty. The participants were then followed up for an average of 10.5 ± 7.5 yr, during which 202 patients suffered incident vertebral fractures. The Cox proportional hazards model for incident vertebral fracture revealed that lumbar bone mineral density (hazard ratio [HR] 0.753, p<.001), adiponectin (HR 1.025, p=.021), log IL-6 (HR 1.227, p=.029), prevalent vertebral fracture (HR 2.124, p<.001), and frailty status (HR 1.355, p=.002) were independent predictors of incident vertebral fractures. The factors associated with frailty status at baseline were assessed using logistic regression analysis, revealing that adiponectin (odds ratio [OR] 1.063, p<.001), log IL-6 (OR 2.94, p<.001), and prevalent vertebral fractures (OR 2.816, p<.001) were significantly associated with current frailty. Biochemical factors such as IL-6 and adiponectin were commonly associated with vertebral fractures and frailty. Additionally, frailty status was identified as an independent risk factor for vertebral fractures, while prevalent vertebral fractures were significantly associated with frailty. These findings clearly indicate that frailty and osteoporotic fractures represent mutual risks for each other, with serum levels of adiponectin and IL-6 serving as common physiological backgrounds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".