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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".