Network Meta-Analysis of Non-Conventional Therapies for Improving Upper Limb Motor Impairment Poststroke
Bibliographic record
Abstract
BACKGROUND: Network meta-analysis is a method that can estimate relative efficacy between treatments that may not have been compared directly within the literature. The purpose of this study is to present a network meta-analysis of non-conventional interventions to improve upper extremity motor impairment after stroke. METHODS: A literature search was conducted in 5 databases from their inception until April 1, 2021. Terms were used to narrow down articles related to stroke, the upper extremity, and interventional therapies. Randomized controlled trials written in English were eligible if; 50% poststroke patients; ≥18 years old; applied an intervention for the upper extremity, and/or used the Fugl-Meyer upper extremity scale as an outcome measure; the intervention had ≥3 randomized controlled trials with comparisons against a conventional care group; conventional care groups were dose matched for therapy time. A Bayesian network meta-analysis approach was taken to estimate mean difference (MD) and 95% CI. RESULTS: One hundred seventy-six randomized controlled trials containing 6781 participants examining 20 non-conventional interventions were identified for inclusion within the final model. Eight of the identified interventions proved significantly better than conventional care, with modified constraint induced movement therapy (MD, 6.7 [95% CI, 4.3-8.9]), high frequency repetitive transcranial magnetic stimulation (MD, 5.4 [95% CI, 1.9-8.9]), mental imagery (MD, 5.4 [95% CI, 1.8-8.9]), bilateral arm training (MD, 5.2 [95% CI, 2.2-8.1]), and intermittent theta-burst stimulation (MD, 5.1 [95% CI, 0.62-9.5]) occupying the top 5 spots according to the surface under the cumulative ranking curve. CONCLUSIONS: Overall, it would seem that modified constraint induced movement therapy has the greatest probability of being the most effective intervention, with high-frequency repetitive transcranial magnetic stimulation, mental imagery, and bilateral arm training all having similar probabilities of occupying the next spot in the rankings. We think this analysis can provide a guide for where future resources and clinical trials should be directed, and where a clinician may begin when considering alternative therapeutic interventions.
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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 | 0.038 | 0.079 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.053 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".