The Effectiveness of Transcranial Magnetic Stimulation for Obsessive-Compulsive Disorder
Bibliographic record
Abstract
Obsessive-compulsive disorder (OCD) is an anxiety-related disorder. Obsessions are experienced as recurring, unwanted thoughts. In response to obsessions, people feel driven to act with repetitive behaviours, known as compulsions. Obsessions and compulsions cause significant distress and impairment in an individual’s daily life and relationships. OCD is relatively common, with a lifetime prevalence rate between 2%-3%. Thus, research must find an effective treatment for individuals with OCD. This paper examines a relatively new area of research that explores the use of transcranial magnetic stimulation (TMS) to treat OCD. TMS is a non-invasive brain stimulation that has recently shown promising effects in treating psychological disorders. Studies researching TMS on OCD, primarily with individuals who have not responded to previous treatments, have found it to be effective, with symptoms significantly decreasing post-treatment, and with effects lasting up to 3-months post-treatment. Overall, TMS appears to offer an effective alternative for individuals with treatment-resistant OCD. However, while studies on TMS show significant results, participants with greater OCD symptoms and/or sleep disturbances tend to report a decreased response to treatment. Also, studies using TMS typically have limitations with their sample sizes, lack of control groups, and lack of long-term follow-up assessments. Overall, TMS appears to be effective for OCD, but future research needs to address current limitations to determine the true effectiveness of this new treatment.
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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 | 0.001 |
| 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) | 0.002 | 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".