rTMS Shows Efficacy in Improving Negative Symptoms in Early Psychosis: A Systematic Review
Bibliographic record
Abstract
Introduction: This systematic review aims to synthesize evidence for use of repetitive transcranial magnetic stimulation (rTMS) in recently diagnosed psychotic disorders to determine whether rTMS could be an effective treatment for early psychosis. Methods: PubMed, CINAHL, and PsycInfo were searched to obtain relevant articles published between 2012 and 2023. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Articles was used as a guideline for reporting. Two authors independently screened articles using inclusion/exclusion criteria. Those meeting criteria were examined in detail, with bias scoring via the Cochrane Risk of Bias tool. Results: Of 2155 articles yielded from the initial search, fifteen ultimately met inclusion/exclusion criteria and were examined further. Most of the studies used the PANSS to assess outcome, and the majority (89%) reported significant effects on negative symptoms following rTMS, with mixed outcomes for positive symptoms. The most common neural target for stimulation was the dorsolateral prefrontal cortex. Seventy-three percent of articles examined neural correlates of outcome, linking outcome to functional connectivity, grey matter volume, and BDNF levels. The majority of studies were rated as some or high bias, due to lack of rigorous controls, lack of blinding, or lack of randomization. Conclusions: Our systematic review suggests that rTMS is effective for treatment of negative symptoms of early psychosis. Negative symptoms are of particular clinical import as a treatment target, given their impact on function, their resistance to conventional treatments, and prognostic significance. Limitations and future directions are discussed.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".