Adjunctive Intermittent Theta-Burst Stimulation for Schizophrenia: A Systematic Review and Meta-analysis of Randomized Double-Blind Controlled Studies
Bibliographic record
Abstract
Objective: In order to determine whether intermittent theta-burst stimulation (iTBS) is a viable adjunct treatment for schizophrenia, a meta-analysis of double-blind, randomized clinical trials (RCTs) was performed. Methods: Four independent researchers extracted and synthesized data from RCTs on adjunctive iTBS for patients suffering from schizophrenia. RevMan 5.3 software was used to calculate risk ratios (RRs) and standardized mean differences (SMDs) along with their 95% confidence intervals (CIs). Results: Fifteen RCTs involving 671 patients with schizophrenia were included. Adjunctive iTBS was significantly superior to sham interventions for improvement in overall psycho-pathology (SMD = −0.75, 95% CI: −1.10, −0.41, I2 = 64%, P < .0001), negative symptoms (SMD = −0.76, 95% CI: −1.18, −0.35, I2 = 78%, P = .0003), and general psychopathology (SMD = −0.51, 95% CI: −0.88, −0.14, I2 = 71%, P = .007), though no significant group dif-ference was found regarding positive symptoms. Adjunctive iTBS also demonstrated superiority over control treatments in improving cognitive functions as measured by the Spatial Span Test (SMD = 0.83, 95% CI: 0.16, 1.49, I2 = 73%, P = .02) and Montreal Cognitive Assessment (SMD = 0.49, 95% CI: 0.11, 0.88, I2 = 0%, P = .01). Discontinuation rates (RR = 0.92, 95% CI: 0.57, 1.50, I2 = 0%, P = .75) and adverse events were comparable between groups. Conclusion: The use of iTBS in patients with schizophrenia appears to be effective in improving psychiatric symptoms and cognitive function. To substantiate these prelimi-nary findings, future research involving larger participant cohorts is 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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.029 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.004 | 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".