Bibliographic record
Abstract
In my practice, patients with migraine frequently cannot tolerate even low doses of topiramate. In contrast, patients with epilepsy can usually take quite high doses of this medication. This difference between migraine and epilepsy is reflected in experimental trials in these conditions. For example, in two studies of treatment with topiramate (1,2), 44% of migraine patients withdrew from 125mg per day, whereas only 17% of patients with epilepsy withdrew from a much higher dose of 1000mg. However, it should also be noted that, in the same studies, patients with migraine withdrew at a higher rate of 10% from placebo, in comparison to only 2% in patients with epilepsy. The intolerance to topiramate may be specific, or a reflection of a more general problem with medication. A specific intolerance in migraine could be due to 1) altered metabolism of topiramate resulting in higher levels, or 2) abnormal neuronal systems that have an increased vulnerability to its actions. A more general problem might be explained by 1) enhancement of side effects due to heightened sensitivity to stimulation, or 2) difficulty participating in any treatment, even placebo, because of associated anxiety. Intolerance to topiramate, specific or otherwise, limits its use in migraine and deserves further investigation. The first step would be a systematic review of the clinical trials of all drugs used to treat both migraine and other conditions, comparing withdrawal rates from placebo as well as active 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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".