Transcranial magnetic stimulation as a novel approach to effectively treat musical obsession (stuck song syndrome): a case series and a systematic review of case reports
Bibliographic record
Abstract
This is the first case series that demonstrates the use of transcranial magnetic stimulation (TMS) for the treatment of musical obsessions or stuck song syndrome (SSS), following a systematic review that identified existing treatments. SSS can occur independently, but in literature it is commonly reported as an obsessive-compulsive disorder (OCD) or major depressive disorder (MDD) symptom. Most people are familiar with earworms, experienced by up to 98% of the western population. Earworms can become severe leading to the SSS diagnosis. SSS is a distressing repetition of involuntary tunes persisting in one's mind. According to literature, SSS has often been treated using antidepressants which are used to treat MDD and OCD. As TMS has shown a positive therapeutic effect for psychiatric disorders particularly MDD and OCD, we hypothesized that TMS could be an effective treatment that reduces symptoms of patients with SSS. We present two cases of TMS treatment contributing to a reduction in symptoms of SSS. We also aim to provide a systematic review of cases where SSS has been described and compare the pharmacological or psychotherapeutic treatments used with our novel TMS interventions for SSS. This report highlights some limitations, including patient's psychiatric comorbidities and treatment protocol changes, which affect the findings generalizability. Despite these limitations, TMS appears promising as a treatment for SSS due to the observed effectiveness in reducing SSS symptoms and minimal side effects especially in medication-resistant cases.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".