Effects of transcranial direct current stimulation (tDCS) on motor function among people with stroke: evidence mapping
Bibliographic record
Abstract
OBJECTIVE: To present, organize, and assess the methodological quality of the current research related to tDCS on motor function after a stroke and to identify gaps and clinical implications using an evidence mapping approach. METHODS: Six electronic databases (PubMed, Embase, Cochrane Library, Web of Science, CINAHL, PEDro), gray literature, and reference lists of articles were searched from inception until October 2023. The Assessment of Multiple Systematic Reviews 2 (AMSTAR-2) checklist and PEDro scale were used to assess the methodology quality of systematic reviews (SRs) and randomized controlled trials (RCTs). RESULTS: A total of 172 articles met the inclusion criteria from 5759 records, including 46 SRs and 126 RCTs. Related studies came from 29 countries around the world, and China has performed the most, with 12 SRs and 21 RCTs. More than half of SRs (65.22%) were evaluated with low or critically low quality, while 78.58% of RCTs have shown excellent or good quality. A total of 26 SRs and 93 RCTs have reported outcomes on upper limb motor function with kinds of tDCS, and 15 SRs and 44 RCTs have focused on lower extremity function. Studies with safety concerns have reported no or mild adverse events. CONCLUSIONS: This study systematically identified gaps and indicated that tDCS is a kind of potential and safe intervention. Given potential concerns on the clinical application, more high-quality research with large sample size and kinds of objectives is needed in the future.
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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 | 0.034 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.021 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".