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Record W2154442235 · doi:10.1111/epi.12294

Development of new treatment approaches for epilepsy: Unmet needs and opportunities

2013· review· en· W2154442235 on OpenAlexaff
Jacqueline A. French, H. Steve White, Henrik Klitgaard, Gregory L. Holmes, Michael Privitera, Andrew J. Cole, Ellinor Quay, Samuel Wiebe, Dieter Schmidt, Roger J. Porter, Alexis Arzimanoglou, Eugen Trinka, Emilio Perucca

Bibliographic record

VenueEpilepsia · 2013
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTolerabilityEpilepsyMedicineClinical trialDiseaseAdverse effectPreclinical researchIntensive care medicinePharmacologyPsychiatryInternal medicineMedical physics

Abstract

fetched live from OpenAlex

A working group was created to address clinical "gaps to care" as well as opportunities for development of new treatment approaches for epilepsy. The working group primarily comprised clinicians, trialists, and pharmacologists. The group identified a need for better animal models for both efficacy and tolerability, and noted that animal models for potential disease-modifying or antiepileptogenic effect should mirror conditions in human trials. For antiseizure drugs (ASDs), current animal models have not been validated with respect to their relationship to efficacy in common epilepsy syndromes. The group performed an "expert opinion" survey of perceived efficacy of the available ASDs, and identified a specific unmet need for ASDs to treat tonic-atonic and myoclonic seizures. No correlation has as yet been demonstrated between animal models of tolerability and adverse effects (AEs), versus tolerability in humans. There is a clear opportunity for improved therapies in relation to dose-related AEs. The group identified common and rare epilepsy syndromes that could represent opportunities for clinical trials. They identified opportunities for antiepileptogenic (AEG) therapies in both adults and children, acknowledging that the presence of a biomarker would substantially improve the chances of a successful trial. However, the group acknowledged that disease-modifying therapies (given after the first seizure or after the development of epilepsy) would be easier to study than AEG therapies.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.402
GPT teacher head0.390
Teacher spread0.012 · 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

Citations57
Published2013
Admission routes1
Has abstractyes

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