Epilepsy with catamenial pattern
Bibliographic record
Abstract
Catamenial pattern epilepsy is defined as an increase in the frequency of seizures during a specific stage of the menstrual cycle compared to baseline. It has been described that around a third of women with epilepsy have a catamenial pattern. The changes in the seizure pattern would be explained by the influence of catamenial fluctuations, of female gonadal hormones on neuronal excitability. Progesterone through its metabolite allopregnanolone plays a protective role by increasing GABAergic transmission; however, its effect on brain progesterone receptors can increase neuronal excitability. The effects of estrogens are complex, they tend to increase neuronal excitability, although their effects depend on their concentration and exposure time. Three catamenial patterns of seizure exacerbation have been proposed: the perimenstrual pattern, the periovulatory pattern, and the luteal pattern. The diagnostic approach is carried out through a systematic process of 4 steps: a) clinical history of the pattern of the menstrual cycle and epileptic seizures; b) diagnostic methods to characterize the menstrual cycle and the pattern of seizures; c) check diagnostic criteria; and d) categorize the catamenial pattern. The treatment options studied require a higher level of evidence, and there is no specific treatment. Optimization of conventional antiseizure treatment is recommended as the first therapeutic option. Other therapeutic options, such as non-hormonal and hormonal treatments, could be useful in case the first therapeutic option proves to be ineffective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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 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".