Characteristics of Headache Associated with Intractable Partial Epilepsy
Bibliographic record
Abstract
PURPOSE: The association between headache (HA) and epilepsy is well known. However, few previous studies characterized HA types and head sensations (HSens) in large populations of individuals with well-defined forms of epilepsy. METHODS: To analyze the incidence of HA in such a group, we compare HA and non-HA patients to identify special predictive factors for HAs or migraine. We also investigate the pathologically verified group for possible correlations with HAs or migraine. One hundred consecutive patients undergoing presurgical evaluation for pharmacologically intractable partial epilepsy were interviewed. For each HA type, we inquired about lateralization, localization, quality of HA, and results of treatment. RESULTS: Periictal HAs were reported by 47 patients. Of those, 11 had preictal HA (PIHA), and 44 had postictal HA (PostHA). Eight patients had both PIHA and PostHA. Interictal HAs (InterHAs) were reported by 31 patients. Twenty-nine (62%) of 47 patients had frontotemporal HAs. Twenty-five patients had migraine-like HA without aura: 18 (60%) of 30 patients with temporal lobe epilepsy (TLE) and seven (41%) of 17 with extratemporal epilepsy (ETE). No correlation between pathology and presence of HA was found in 59 pathologically verified patients, except in four who had arteriovenous malformations (AVMs): three had and one did not have HAs. Eighteen patients had, in addition, poorly localized and ill-described HSens other than HAs. CONCLUSIONS: We confirm an association between focal epilepsy and HAs, including migraine without aura. This is true for both TLE and ETE. PIHA and even prodromal HA may be related to the epileptic discharge and may have lateralizing value. This association is not recognized by the current International Headache Society (IHS) classification. The presence of HA and migraine is not related to the underlying epileptogenic pathology except in patients with AVMs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".