LEAN MASS AS AN IMPORTANT PREDICTOR FOR BONE MINERAL CONTENT OF THE HEMIPARETIC UPPER EXTREMITY IN CHRONIC STROKE: IMPLICATIONS FOR REHABILITATION.
Bibliographic record
Abstract
PURPOSE/HYPOTHESIS: (1) To evaluate bone mineral content (BMC) and lean mass of the arms in individuals with chronic stroke (onset >1 year), (2) To determine the predictors for paretic arm BMC and lean mass. NUMBER OF SUBJECTS: 57. MATERIALS/METHODS: Fifty-seven individuals (50 years of age or more) with chronic stroke underwent a total body scan using Dual-energy X-ray Absorptiometry (DXA). BMC and lean mass of both arms were determined from the scans. The paretic arm was also evaluated for (1) muscle strength (hand-held dynamometry),(2) motor function (Wolf Motor Functional Test), and (3) amount of use (amount of use scale in the Motor Activity Log). RESULTS: The paretic arm showed a significant 14.8% (p<0.001) and 10.7% (p<0.001) lower BMC and lean mass, respectively, than the non-paretic arm. Multiple regression analyses showed that paretic arm lean mass was the most important predictor of paretic arm BMC, accounting for 73.9% of its variance (p<0.001) while muscle strength accounted for 7.8% of the variance in paretic arm lean mass (p<0.001). Paretic arm strength was highly correlated with Wolf Motor Function Test score (r= 0.720, p<0.001) and amount of use scale in the Motor Activity Log (r=0.642, p<0.001). CONCLUSIONS: Individuals with chronic stroke have significant bone loss and muscle atrophy in the paretic arm. Paretic arm lean mass is the most important predictor of its BMC. Muscle strength, on the other hand, is a significant predictor of the paretic arm lean mass. CLINICAL RELEVANCE: Rehabilitation should include strength training for improving muscle and bone health of the paretic arm.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".