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Record W4225129410 · doi:10.33588/rn.7409.2022041

Epilepsy with catamenial pattern

2022· review· en· W4225129410 on OpenAlexaff
Gaby Moscol, Poul Espino, Luis Carlos Mayor, Jorge G. Burneo

Bibliographic record

VenueRevista de Neurología · 2022
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsWestern University
Fundersnot available
KeywordsEpilepsyPsychologyMedicineNeuroscience

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.052
GPT teacher head0.345
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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