Relationships between calf muscle density and muscle strength, mobility and bone status in the stroke survivors with subacute and chronic lower limb hemiparesis.
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship between muscle density and neuromusculoskeletal status in stroke survivors with subacute and chronic hemiparesis. METHODS: Community-dwelling adults were recruited into one of 3 groups (11 per group): subacute stroke group (SSG, <6 months post-stroke), chronic stroke group (CSG, >1 year post-stroke), or age- and gender-matched control group (CG). Muscle density, muscle mass and tibial bone status (cortical density, mass and polar stress-strain index (pSSI)) were measured bilaterally at the tibial 66% site using peripheral quantitative computed tomography. Muscle strength of ankle plantarflexors and knee extensors was assessed using isokinetic dynamometry. Mobility was assessed using the Berg Balance Scale. Univariate regression analyses by group tested whether side-to-side differences in muscle density and measures of neuromusculoskeletal status were related. RESULTS: In the SSG and CG, relationships were observed for muscle density and ankle plantarflexor strength (R²= 0.365 and 0.503). Muscle density related to muscle mass in the CG only (R²= 0.889). Muscle density related to cortical bone density in the SSG (R²= 0.602) and pSSI in the CSG (R²= 0.434). CONCLUSIONS: Muscle density may provide insight into the side-to-side changes in muscle and bone strength following hemiparetic stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".