Exploring new balance and gait factors that are associated with osteosarcopenia in patients with a previous fall and/or fracture history
Bibliographic record
Abstract
Osteosarcopenic individuals have poor muscle function and increased bone fragility, which results in a severe detriment to health outcomes. Hence, there is a necessity to discover easily accessible factors associated with osteosarcopenia to develop timely interventions. This study aimed to determine new sensitive balance and/or gait variables that are associated with osteosarcopenia in a population of older people with a history of falls and/or fractures. In a cross-sectional cohort study, 306 men and women aged ≥65 years completed a series of questionnaires, clinical assessments and muscle strength and function tests. Subsequently, participants were separated into osteopenia, osteoporosis and osteosarcopenia, groups for comparison and further analysis. Osteosarcopenia performed worse than osteopenia and osteoporosis in grip strength, gait speed, physical function scores and in multiple gait and balance indices (p<0.001). During posturography testing, there were larger elliptical areas with eyes open (p = 0.003), and eyes closed (p = 0.043) and increased sway velocity on a firm platform (p = 0.007) in the osteosarcopenia group, compared to osteoporosis. Limits of stability and eyes open ellipse area significantly contributed to the multivariable model (p = 0.029 and p = 0.038, respectively), suggesting that these balance parameters, along with grip strength, may be useful in identifying older adults with osteosarcopenia from those with only osteopenia/osteoporosis. Older adults with osteosarcopenia and a history of falls and/or fractures demonstrated inferior strength, function, and gait characteristics. This study identified indices of balance that were sensitive discriminators for osteosarcopenia and could be easily implemented into routine assessment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".