Lower-extremity muscle atrophy and fat infiltration after chronic spinal cord injury.
Bibliographic record
Abstract
BACKGROUND: Atrophy and fatty-infiltration of lower-extremity muscle after spinal cord injury (SCI) predisposes individuals to metabolic disease and related mortality. OBJECTIVES: To determine the magnitude of atrophy and fatty-infiltration of lower-extremity muscles and related factors in a group of individuals with chronic SCI and diverse impairment. METHODS: Muscle cross-sectional area and density were calculated from peripheral quantitative computed tomography scans of the 66% site of the calf of 70 participants with chronic SCI [50 male, mean age 49 (standard deviation 12) years, C2-T12, AIS A-D] and matched controls. Regression models for muscle area and density were formed using 16 potential correlates selected a priori. RESULTS: Participants with motor-complete SCI had ≈ 32% lower muscle area, and ≈ 43% lower muscle density values relative to controls. Participants with motor-incomplete SCI had muscle area and density values that were both ≈ 14% lower than controls. Body mass (+), tetraplegia (+), motor function (+), spasticity (+), vigorous physical activity (+), wheelchair use (-), age (-), and waist circumference (-) were associated with muscle size and/or density in best-fit regression models. CONCLUSIONS: There are modifiable factors related to muscle size, body composition, and activity level that may offer therapeutic targets for preserving metabolic health after chronic SCI.
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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) | 0.000 | 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) | 0.003 | 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".