Daily listening to Mozart reduces seizures in individuals with epilepsy: A randomized control study
Bibliographic record
Abstract
OBJECTIVE: Epilepsy is one of the most common neurological disorders . Many individuals continue to have seizures despite medical and surgical treatments, suggesting adjunctive management strategies are required. Promising effects of daily listening to Mozart on reducing seizure frequency in individuals with epilepsy have been demonstrated over the last 20 years, but not in a rigorously controlled manner. In this study, we compared the effect on seizure frequency of daily listening to either Mozart K.448 or a spectrally similar, yet non-rhythmic control piece. We hypothesized that there would be no difference in seizure counts when participants listened to Mozart K.448 vs when they listened to the control piece. METHODS: We employed a randomized crossover design, in which each participant was exposed to both three months of daily listening to the first six minutes of Sonata for two pianos in D major by Mozart (Mozart K.448; treatment period) and three months of daily listening to phase-scrambled version (control period). There was a three-month baseline and a three-month follow-up period before and after the six-month listening period, respectively. Change in seizure counts obtained from the seizure diaries was considered as the main study outcome. RESULTS: < .001). SIGNIFICANCE: Using a spectrally similar control piece, our study advances previous reports that were limited by a "no music" control condition. Daily listening to Mozart K.448 was associated with reducing seizure frequency in adult individuals with epilepsy. These results suggest that daily Mozart listening may be considered as an adjunctive therapeutic option to reduce seizure burden in individuals with epilepsy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".