Bone Loss and Muscle Atrophy in Spinal Cord Injury: Epidemiology, Fracture Prediction, and Rehabilitation Strategies
Bibliographic record
Abstract
Individuals with spinal cord injury (SCI) often experience bone loss and muscle atrophy. Muscle atrophy can result in reduced metabolic rate and increase the risk of metabolic disorders. Sublesional osteoporosis predisposes individuals with SCI to an increased risk of low-trauma fracture. Fractures in people with SCI have been reported during transfers from bed to chair, and while being turned in bed. The bone loss and muscle atrophy that occur after SCI are substantial and may be influenced by factors such as completeness of injury or time postinjury. A number of interventions, including standing, electrically stimulated cycling or resistance training, and walking exercises have been explored with the aim of reducing bone loss and/or increasing bone mass and muscle mass in individuals with SCI. Exercise with electrical stimulation appears to increase muscle mass and/or prevent atrophy, but studies investigating its effect on bone are conflicting. Several methodological limitations in exercise studies with individuals with SCI to date limit our ability to confirm the utility of exercise for improving skeletal status. The impact of standing or walking exercises on muscle and bone has not been well established. Future research should carefully consider the study design, skeletal measurement sites, and the measurement techniques used in order to facilitate sound conclusions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".