The Effect of Prophylactic Medications on TMS for Migraine Aura
Bibliographic record
Abstract
PURPOSE: Low frequency transcranial magnetic stimulation (TMS) has recently been shown to be effective for the acute treatment of migraine with aura. TMS has recently been shown to inhibit cortical spreading depression (CSD). Prophylactic medications (PM) may reduce the frequency of migraine attacks by elevating CSD threshold. The interaction between PM and TMS is unknown. METHODS: Subgroup analysis was performed on a double-blind, Sham-controlled study that evaluated the efficacy and safety of TMS for the acute treatment of migraine with aura. Analysis of the primary efficacy endpoint pain-free at 2 hours (pain-free rate [PFR]) between TMS and Sham groups was performed based on the non-randomized use of PM. RESULTS: A total of 164 subjects eligibly treated at least 1 migraine with aura attack with TMS (n = 82) or Sham stimulation (n=82). Baseline pain intensity at the time of treatment for the first attack was no pain (31%), mild (40%), moderate (23%), or severe pain (6%). PM were used by 37% (31/82) and 41.5% (34/82) in the Sham- and TMS-treated patients, respectively. Sham patients on no PM (Sham without) had significantly higher PFR than Sham-treated patients on PM (Sham with) (P = .0014). There was no difference in PFR between TMS-treated patients on (TMS with) or off (TMS without) PM (P = .5513). However, TMS with had significantly higher PFR than Sham with patients (P= .002). There was no difference in PFR between TMS without and Sham without patients (P = .4061). CONCLUSION: Prophylactic medications do not appear to influence the treatment response to TMS. The better response in Sham-treated patients not on PM may indicate a more responsive subgroup or different patient phenotype than those currently using PM. These findings will need to be verified in a larger patient sample randomized by presence or absence of PM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".