Transcranial direct current stimulation for acute major depressive episodes: Meta-analysis of individual patient data
Bibliographic record
Abstract
BACKGROUND: Transcranial direct current stimulation (tDCS) is a non-pharmacological intervention for depression. It has mixed results, possibly caused by study heterogeneity. AIMS: To assess tDCS efficacy and to explore individual response predictors. METHOD: Systematic review and individual patient data meta-analysis. RESULTS: Data were gathered from six randomised sham-controlled trials, enrolling 289 patients. Active tDCS was significantly superior to sham for response (34% v. 19% respectively, odds ratio (OR) = 2.44, 95% CI 1.38-4.32, number needed to treat (NNT) = 7), remission (23.1% v. 12.7% respectively, OR = 2.38, 95% CI 1.22-4.64, NNT = 9) and depression improvement (B coefficient 0.35, 95% CI 0.12-0.57). Mixed-effects models showed that, after adjustment for other predictors and confounders, treatment-resistant depression and higher tDCS 'doses' were, respectively, negatively and positively associated with tDCS efficacy. CONCLUSIONS: The effect size of tDCS treatment was comparable with those reported for repetitive transcranial magnetic stimulation and antidepressant drug treatment in primary care. The most important parameters for optimisation in future trials are depression refractoriness and tDCS dose.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".