Predictors of efficacy, tolerability and discontinuation of Transcranial Direct Current Stimulation (tDCS) for Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD): a meta-analysis and meta-regression
Bibliographic record
Abstract
Numerous patients with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) are refractory to pharmacological treatment, and non-invasive brain neurostimulation has been investigated as another possibility for improving cognition. The performed meta-analysis and meta-regression verified predictors of efficacy, tolerability, and discontinuation of transcranial direct current stimulation (tDCS) for treating MCI or AD. The analyzed studies used the Mini-Mental State Exam, Montreal Cognitive Assessment, or Alzheimer's Disease Assessment Scale - Cognitive Subscale scores as outcome measures. Databases (PubMed, Embase, and Web of Science - primary collection) were searched, resulting in 12 published randomized and controlled trials. The risk of bias assessment was based on Cochrane Review recommendations, considering study characteristics. Other evaluated outcomes were the number of adverse effects (tolerability) and dropouts. Overall and anodal tDCS improved cognition compared to the sham protocol. Group comparisons did not show statistically significant differences for adverse effects and dropouts. Session duration was a response predictor, as stimulations of up to 20 minutes for ten days or more improved the outcome achievement. The AD diagnosis covariate also affected efficacy. The findings should be interpreted carefully in clinical practice because the stimulation effect may vary among subjects.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.059 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".