The efficacy of transcranial magnetic stimulation (TMS) for negative symptoms in schizophrenia: a systematic review and meta-analysis
Bibliographic record
Abstract
Several trials have shown preliminary evidence for the efficacy of transcranial magnetic stimulation (TMS) as a treatment for negative symptoms in schizophrenia. Here, we synthesize this literature in a systematic review and quantitative meta-analysis of double-blind randomized controlled trials of TMS in patients with schizophrenia. Specifically, MEDLINE, EMBASE, Web of Science, and PsycINFO were searched for sham-controlled, randomized trials of TMS among patients with schizophrenia. The effect of TMS vs. sham on negative symptoms in each study was quantified by the standardized mean difference (SMD, Cohen's d) with 95% confidence intervals (95%CI) and pooled across studies using an inverse variance random effects model. We identified 57 studies with a total of 2633 participants that were included in the meta-analysis. The pooled analysis showed statistically significant superiority of TMS (SMD = 0.41, 95%CI: 0.26; 0.56, p-value < 0.001), corresponding to a number needed to treat of 5. Furthermore, stratified analyses suggested that TMS targeting the left dorsolateral prefrontal cortex and using a stimulation frequency >1 Hz was most efficacious. There was, however, substantial heterogeneity and high risk of bias among the included studies. In conclusion, TMS appears to be an efficacious treatment option for patients with schizophrenia suffering from negative symptoms, but the optimal TMS parameters are yet to be established.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.006 | 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 | 0.002 |
| 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".