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Hypothalamic Hamartoma and Seizures: A Treatable Epileptic Encephalopathy

2003· article· en· W2145074684 on OpenAlexaff
Samuel F. Berkovic, Alexis Arzimanoglou, Ruben Kuzniecky, A. Simon Harvey, André Palmini, Frédérick Andermann

Bibliographic record

VenueEpilepsia · 2003
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersGlaxoSmithKlinePfizer
KeywordsHypothalamic hamartomaGelastic seizureLesionHamartomaMedicineEpilepsyRadiosurgerySeizure typesPediatricsSurgeryPrecocious pubertyPathologyPsychiatryInternal medicineHormone

Abstract

fetched live from OpenAlex

Hypothalamic hamartomas may be associated with gelastic seizures, focal seizures, and a generalized epileptic encephalopathy, with severe seizures and cognitive and behavior decline. Despite earlier views to the contrary, good evidence now exists that all these clinical features are caused, directly or indirectly, by the hamartoma. Resection of these lesions was long regarded as too hazardous and unlikely to benefit seizure control. It is now clear that hypothalamic hamartomas can be effectively treated with a variety of surgical approaches with sustained seizure control and often seizure freedom. Qualitative observations suggest that behavior and cognition also improve with treatment, but quantitative validation is required. The specific approach should be tailored according to the surgical anatomy of the lesion and the experience of the surgeon. Choices include a transcallosal approach (good for intraventricular lesions), a pterional approach (useful for interpeduncular lesions), a transventricular endoscopic approach, or destruction of the lesion with radiofrequency probes or gamma knife radiosurgery. The previously dismal outlook for children with severe seizures associated with this lesion has now dramatically changed. These insights may have implications for other epileptic encephalopathies of childhood.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.267
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations176
Published2003
Admission routes1
Has abstractyes

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