Effects of Exercise Training and Inspiratory Muscle Training in Spinal Cord Injury: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To provide a systematic review of the studies assessing exercise training and inspiratory muscle training (IMT) in individuals for the improved respiratory function of patients with spinal cord injury (SCI). METHODS: Thirteen studies (5 exercise training, 8 IMT) were identified. Articles were scored for their methodological quality using the Physiotherapy Evidence Database scores and Downs and Black tools for randomized and nonrandomized studies, respectively. Conclusions were based on the most rigorously executed studies using Sackett's levels of evidence. RESULTS: Study comparison was compromised by diverse research designs; small sample sizes; and heterogeneity of studied populations, protocols, and outcome measures. Based on current literature, there is level 2 evidence supporting exercise training as an intervention to improve respiratory strength and endurance and level 4 evidence to support exercise training as an intervention that might improve resting and exercising respiratory function in people with SCI. There is level 4 evidence to support IMT as an intervention that might decrease dyspnea and improve respiratory function in people with SCI. CONCLUSIONS: There are insufficient data to strongly support the use of exercise training or IMT for improved respiratory function in people with SCI. There is some evidence of efficacy of both regimens; however, the evidence is not of the best possible quality.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".