Transcranial direct current stimulation and its effect on cognitive symptoms of schizophrenia: An updated review
Bibliographic record
Abstract
Objective: Cognitive impairments in schizophrenia significantly affect functional outcomes and quality of life. This meta-analysis evaluates the effectiveness of transcranial direct current stimulation (tDCS) as an intervention for cognitive deficits in individuals with schizophrenia. Methods: From May 20 to June 15, 2024, a systematic search of PubMed, Medline, Embase, and the Cochrane central register of controlled trials was conducted. After applying eligibility criteria, 13 randomized sham-controlled trials were included, involving 261 participants in the tDCS group and 247 in the sham group. Standardized mean difference (SMD) was computed to measure the effect size of cognitive outcomes. Statistical analyses were performed using a random-effects model to account for heterogeneity. Results: = 44 %) was observed, attributed to variations in tDCS protocols, participant demographics, and cognitive assessment tools. Although certain studies showed improvements in specific domains like working memory, the overall impact of tDCS on cognitive symptoms was not statistically significant. Conclusions: This meta-analysis underscores the lack of significant evidence for tDCS in improving cognitive deficits in schizophrenia. The findings highlight the urgent need for standardizing tDCS protocols and employing domain-specific cognitive assessments. This standardization, along with the collection of more domain-specific data, is crucial for future research and the improvement of current methodologies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".