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Record W2053034948 · doi:10.1155/2015/831260

Early Use of the NMDA Receptor Antagonist Ketamine in Refractory and Superrefractory Status Epilepticus

2015· review· en· W2053034948 on OpenAlexaff
Frederick A. Zeiler

Bibliographic record

VenueCritical Care Research and Practice · 2015
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsStatus epilepticusNMDA receptorMedicineKetamineExcitotoxicityRefractory (planetary science)Glutamate receptorEpilepsyAntagonistPharmacologyAnesthesiaReceptorNeuroscienceInternal medicinePsychiatryPsychologyBiology

Abstract

fetched live from OpenAlex

Refractory status epilepticus (RSE) and superrefractory status epilepticus (SRSE) pose a difficult clinical challenge. Multiple cerebral receptor and transporter changes occur with prolonged status epilepticus leading to pharmacoresistance patterns unfavorable for conventional antiepileptics. In particular, n-methyl-d-aspartate (NMDA) receptor upregulation leads to glutamate mediated excitotoxicity. Targeting these NMDA receptors may provide a novel approach to otherwise refractory seizures. Ketamine has been utilized in RSE. Recent systematic review indicates 56.5% and 63.5% cessation in seizures in adults and pediatrics, respectively. No complications were described. We should consider earlier implementation of ketamine or other NMDA receptor antagonists, for RSE. Prospective study of early implementation of ketamine should shed light on the role of such medications in RSE.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.319
GPT teacher head0.517
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations38
Published2015
Admission routes1
Has abstractyes

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