Efficacy of transcranial direct current stimulation in mild cognitive impairment – a meta‐analysis
Bibliographic record
Abstract
Abstract Background MCI or Mild Cognitive Impairment is a state of cognitive decline which is in between that of normal ageing and dementia. People with MCI have a higher chance of progressing to dementia because of which introducing interventions at this stage becomes increasingly important. Transcranial Direct Current Stimulation (tDCS) is one non‐invasive brain stimulation technique that is considered as an effective intervention strategy for people with MCI to prevent their progression to dementia. Method From 221 and 686 results obtained from PubMed and Google Scholar respectively using the search term ‘(“tDCS”) AND (“MCI”) AND (Randomized) AND (sham controlled)’, 8 studies were included for the current meta‐analysis. Among these studies, the most commonly used cognitive tests namely MMSE (Mini Mental State Examination), MoCA (Montreal Cognitive Assessment), TMT (Trail Making Test) A and B, DST (Digit Span Test) forward and backward and Logical Memory Test were considered for the analysis. The standard mean difference between active and sham treatments in different tests were computed. Result The standard mean difference and the 95% confidence interval for the test scores are MoCA: 0.37 (‐0.22,0.95), MMSE: 0.26 (‐0.25, 0.77), TMT A: ‐0.01 (‐0.42, 0.40), TMT B: 0.07 (‐0.41, 0.55), DST F: ‐0.09 (‐0.44, 0.25), DST B: ‐0.11 (‐0.54, 0.31) and Logical memory test: 0.80 (‐0.24, 1.83). It is found that there is an improvement in the performance in MoCA, MMSE, TMT A and logical memory test after treatment with active tDCS but the improvement is statistically insignificant. Conclusion Although there is an evident clinical effectiveness, the study identified no significant improvement in cognitive performance with active tDCS in comparison with sham tDCS. This might be due to difference in protocols followed among studies, lower duration of stimulation in a few studies and smaller sample size in each.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".