Influence of Exercise on Patients with Guillain-Barré Syndrome: A Systematic Review
Bibliographic record
Abstract
Purpose: To evaluate the effects of exercise interventions on improving physical outcomes in patients with Guillain-Barré syndrome (GBS). Methods: The PubMed database was searched for articles published up to and including February 2015. Randomized controlled trials (RCTs), case reports, and quasi-experimental and single-subject designs published in English-language, peer-reviewed journals that assessed the impact of physical exercise on patients with GBS were included; study quality was assessed using Sackett's rules of evidence. Data are presented qualitatively and quantitatively using numerical values and percentages. Results: Seven articles were included in the systematic review. One RCT showed that high-intensity relative to lower intensity exercise significantly reduced disability in patients with GBS, as measured with the FIM (p<0.005, r=0.71). Overall, various types of exercise programmes improve physical outcomes such as functional mobility, cardiopulmonary function, isokinetic muscle strength, and work rate and reduce fatigue in patients with GBS. Conclusion: Because of insufficient high-quality literature, making confident conclusions about the effects of exercise interventions on physical outcomes in patients with GBS is not possible. Future research should consider using higher quality study designs to confirm the results outlined in this article.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".