Transcranial direct current stimulation-efficacy in mild cognitive impairment: A meta-analysis
Bibliographic record
Abstract
Objectives: Mild cognitive impairment (MCI) is a transition state in which individuals have cognitive abilities that are in between those of normal aging and dementia. Although not everyone with MCI develops dementia, the risk of progression to dementia is higher in people with MCI. Interventions at this stage can prevent or delay the onset of dementia. In recent years, studies on non-invasive brain stimulation techniques, namely transcranial direct current stimulation (tDCS), have gained momentum for cognitive enhancement in MCI. Since there are very few studies that also report varied results, it becomes important to analyze the effect of tDCS in MCI. The aim of this study was to systematically review the available evidence about using tDCS for MCI and to assess its efficacy using meta-analysis. Materials and Methods: Eight single- or double-blinded randomized controlled trials were included in the study. Montreal cognitive assessment (MoCA) and mini-mental state examination (MMSE) for global cognition; and digit span test forward and backward, trail-making test (TMT) A and B; and logical memory test (LMT) assessing specific cognitive domains were considered. A random-effects model was used wherein the standardized mean difference (SMD) and its 95% confidence intervals were reported. Results: The effect of the active tDCS (MoCA [SMD 0.37, 95% CI -0.22-0.95], MMSE [SMD 0.26, 95% CI 0.25-0.77], TMT-A [SMD -0.01, 95% CI -0.42-0.40], and LMT [SMD 0.80, 95% CI -0.24-1.83]) when compared with the sham tDCS was statistically insignificant. Conclusion: The current meta-analysis identified insignificant improvement in cognitive performance with active tDCS treatment as compared to sham tDCS among people with MCI.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.059 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".