Efficacy of non-pharmacological interventions for individuals with amyotrophic lateral sclerosis: systematic review and network meta-analysis of randomized control trials
Bibliographic record
Abstract
This network meta-analysis (NMA) aimed to compare the efficacy of five non-pharmacological interventions, including exercise intervention (EI), nutritional intervention (NI), respiratory intervention (RI), psychological intervention (PSI), and integrated physical intervention (IPI), on functional status, quality of life, muscle strength, pulmonary function, and safety in patients with amyotrophic lateral sclerosis (ALS). We searched nine databases, PubMed, Cochrane, Embase, Scopus, Web of Science, CNKI, CBM, WFPD, and CSTJ, for randomized controlled trials of ALS patients. The primary outcome was the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) score. Secondary outcomes were the McGill Quality of Life Questionnaire (McGill-QoL), Medical Research Council (MRC)-sum score, Forced Vital Capacity (FVC), and Fatigue Severity Scale (FSS) score. This NMA was conducted using random-effect models to calculate the standard mean difference (SMD) and 95% confidence interval (CI). All types of supplemental interventions had some benefit for patients with ALS. EI had a beneficial effect on the ALSFRS-R score (SMD: 1.01; 95% CI 0.50-1.51), FVC (SMD: 0.78; 95% CI 0.02-1.55), McGill-QoL (SMD: 0.71 95% CI 0.33-1.08), and MRC (SMD: 1.11; 95% CI 0.08-2.14). RI had a beneficial effect on the ALSFRS-R score (SMD: 0.83 95% CI 0.12-1.55). IPI had a beneficial effect on the ALSFRS-R score (SMD: 0.65 95% CI 0.06-1.24). NI had a beneficial effect on the McGill-QoL (SMD: 0.63 95% CI 0.02-1.23). The current study findings support a multimodal intervention strategy with an emphasis on EI for slowing disease progression in patients with ALS.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.037 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".