Does cycling induced by functional electrical stimulation enhance motor recovery in the subacute phase after stroke? A systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To investigate the effects of cycling with functional electrical stimulation on walking, muscle power and tone, balance and activities of daily living in subacute stroke survivors. Data Sources: Ten electronic databases were searched from inception to February 2020. Review methods: Inclusion criteria were: subacute stroke survivors (<6 months since stroke), an experimental group performing any type of cycling training with electrical stimulation, alone or in addition to usual care, and a control group performing usual care alone. Two reviewers assessed eligibility, extracted data and analyzed the risks of bias. Standardized Mean Difference (SMD) or Mean Difference (MD) with 95% Confidence Intervals (CI) were estimated using fixed- or random-effects models to evaluate the training effect. Results: Seven randomized controlled trials recruiting a total of 273 stroke survivors were included in the meta-analyses. There was a statistically significant, but not clinically relevant, effect of cycling with electrical stimulation compared to usual care on walking (six studies, SMD [95% CI] = 0.40 [0.13, 0.67]; P = 0.004), capability to maintain a sitting position (three studies, MD [95% CI] = 7.92 [1.01, 14.82]; P = 0.02) and work produced by the paretic leg during pedaling (2 studies, MD [95% CI] = 8.13 [1.03, 15.25]; P = 0.02). No significant between-group differences were found for muscular power, tone, standing balance, and activities of daily living. Conclusions: Cycling training with functional electrical stimulation cannot be recommended in terms of being better than usual care in subacute stroke survivors. Further investigations are required to confirm these results, to determine the optimal training parameters and to evaluate long-term effects.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.026 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".