Topiramate Prophylaxis and Response to Triptan Treatment for Acute Migraine
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of topiramate migraine prophylaxis on subject responsiveness to triptans used for acute symptomatic migraine treatment. BACKGROUND: Clinical experience suggests that prophylactic migraine treatment may enhance the efficacy of symptomatic medications used to treat acute migraine attacks. METHODS: This open-label, single-arm multicenter study consisted of a 6-week baseline period followed by a 16-week topiramate treatment period. Subjects meeting International Headache Society (IHS) criteria for migraine with and without aura signed consent and entered the baseline period. Those with 3 to 12 migraine periods per month during baseline received topiramate prophylactic treatment. Only patients who completed at least 12 weeks of topiramate treatment were included in the data analysis. RESULTS: Of 55 patients screened, 40 subjects entered the topiramate treatment period and 21 subjects received at least 12 weeks of treatment. Mean final dose of topiramate was 124 mg per day (range 50 to 200 mg per day). During the baseline period, the mean percentage of attacks rendered pain-free at 2 hours for the 21 subjects was 46.9% (SD = 31.9), while during the topiramate treatment period it was 44.6% (SD = 32.2) (P= .8). On topiramate, after the first 8 weeks of dosage titration, patients experienced a mean of 3.68 migraine attacks/month, compared to 4.31 during the baseline period (P < .03). Thirteen subjects discontinued because of adverse events. The most commonly reported adverse events were paresthesia, fatigue, anxiety, and dizziness. CONCLUSION: Although topiramate prophylaxis did reduce migraine attack frequency, in this pilot study topiramate prophylactic migraine treatment did not increase the proportion of patients pain-free 2 hours after symptomatic triptan therapy.
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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 | 0.001 | 0.005 |
| 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".