518 – Intermittent Theta Burst Stimulation (ITBS) for the Treatment of Negative Symptoms in Schizophrenia
Bibliographic record
Abstract
Introduction: High frequency repetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral prefrontal cortex (DLPFC) has been shown effective for reducing persistant negative symptoms of schizophrenia. Intermittent theta burst stimulation (iTBS) is a new paradigm of rTMS that allowed more sustained facilitation effect. Aims: The aim of this study is to investigate the effect of theta burst in reduction of persistant negative symptoms in schizophrenia. Methods: 24 adult schizophrenia outpatients were assigned to receive iTBSat 80% motor threshold, or sham TMS over the left DLPFC, daily; for 20 sessions. The primary outcome measure was the Scale for the Assessment of Negative Symptoms (SANS).score. Secondary outcomes included depression as measured with the Calgary Depression Scale (CDS), and cogntion as assessed with digit span and trail making test. Patients were followed-up 6 months afterwards. Results: The primary outcome measure (change in Scale for Assessment of Negative Symptoms score) showed a statistically significant drop at month 1, 3 and 6 for the iTBS group, but not ther placebo groups. Digit span and trail making test score were also significally improved after treatment in iTBS group. Calgary depression scale score did not demontrate any significant change. Conclusions: iTBS may serve as a relatively noninvasive treatment of the negative and neurocognitive deficits associated with schizophrenia. ClinicalTrials.gov Identifier: NCT00875498
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".