Differences in Body Composition Across Levels of Disability Among Ambulatory Patients Living With Multiple Sclerosis
Bibliographic record
Abstract
Few studies have explored body composition differences in relation to disability status in patients with Multiple Sclerosis (MS). PURPOSE: Explore differences in total lean mass, percent body fat, and visceral adiposity mass across mild, moderate, and severely disabled individuals living with MS. METHODS: One hundred and four (M=23, F=81) community dwelling adults diagnosed with MS (mean age =45.69 ± 9.98 y), BMI= 25.79 ± 9,98 kg/m2, participated in this study. All participants were capable of walking 100 meters unassisted and did not regularly participate in physical activity. Regional and whole body composition was assessed using DXA. Self reported disability was quantified according to the Patient Determined Disease Steps (PDDS) self-report questionnaire, whereby participants were categorized as either mild, moderate, or severely disabled. A multivariate analysis of covariance (MANCOVA) was conducted to assess body composition by PDDS, while controlling for age and sex. RESULTS: Analysis revealed that disability status did not have an effect on percent body fat (F (2, 99) = 1.92, p =.15), total body lean tissue (F(2, 99 ) = 1.02, p =.36), or visceral adiposity mass(F (2, 99) = .61, p =.55). Descriptive statistics for body composition by disability status are as follows:Table: No title available.CONCLUSION: Total body lean mass, percent body fat, and visceral adiposity did not differ across levels of disability status. Further work may be needed to explore how body composition variable may interact with other health and physiologic parameters in this important population.
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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 | 0.002 |
| 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) | 0.002 | 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".