Transcranial direct current stimulation for major depression: an updated systematic review and meta-analysis
Bibliographic record
Abstract
Transcranial direct cranial stimulation (tDCS) is a promising non-pharmacological intervention for treating major depressive disorder (MDD). However, results from randomized controlled trials (RCTs) and meta-analyses are mixed. Our aim was to assess the efficacy of tDCS as a treatment for MDD. We performed a systematic review in Medline and other databases from the first RCT available until January 2014. The main outcome was the Hedges' g for continuous scores; secondary outcomes were the odds ratio (ORs) to achieve response and remission. We used a random-effects model. Seven RCTs (n = 259) were included, most with small sample sizes that assessed tDCS as either a monotherapy or as an add-on therapy. Active vs. sham tDCS was significantly superior for all outcomes (g = 0.37; 95% CI 0.04-0.7; ORs for response and remission were, respectively, 1.63; 95% CI = 1.26-2.12 and 2.50; 95% CI = 1.26-2.49). Risk of publication bias was low. No predictors of response were identified, possibly owing to low statistical power. In summary, active tDCS was statistically superior to sham tDCS for the acute depression treatment, although its role as a clinical intervention is still unclear owing to the mixed findings and heterogeneity of the reviewed studies. Further RCTs with larger sample sizes and assessing tDCS efficacy beyond the acute depressive episode are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.022 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".