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Record W4214505087 · doi:10.14740/jmc3870

Anesthetic Care of a Child Harboring the <i>KCNH2</i> Gene

2022· article· en· W4214505087 on OpenAlexvenueno aff
Anuranjan Ghimire, Rita Banoub, Joseph D. Tobias

Bibliographic record

VenueJournal of Medical Cases · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEpilepsyStatus epilepticusGene mutationGeneRepolarizationMutationAnesthesiaBioinformaticsGeneticsInternal medicinePsychiatryBiology

Abstract

fetched live from OpenAlex

Epilepsy is a heterogeneous group of disorders characterized by recurrent and generally unprovoked seizures. Genetic mutations may play an important role in the etiology of epilepsy. Over the past few years, genetic mutations in various genes have been identified in patients with epilepsy. One of the more common mutations responsible for seizures involves the KCNH2 gene. The KCNH2 gene encodes the Kv11.1 protein, which involves the pore-forming subunit of a rapidly activating-delayed rectifier potassium channel. This channel plays an essential role in phases 2 and 3 of the cardiac action potential involving cardiac repolarization as well as being expressed in various parts of the central nervous system where it regulates neuronal function. As such, patients presenting with this gene mutation may be at risk not only for seizures, but also abnormalities in cardiac repolarization leading to lethal arrhythmias. We present an 11-year-old girl who required general anesthesia for magnetic resonance imaging as part of her evaluation for non-convulsive status epilepticus. An epilepsy gene panel evaluated revealed a KCNH2 gene mutation. End-organ involvement of KCNH2 gene mutations is presented, previous reports of anesthetic care for these patients are reviewed, and options for anesthetic care are discussed. J Med Cases. 2022;13(1):40-43 doi: https://doi.org/10.14740/jmc3870

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.510

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 integrity0.0000.001
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 head0.010
GPT teacher head0.264
Teacher spread0.254 · 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 designCase report
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

Citations5
Published2022
Admission routes1
Has abstractyes

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