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Record W3109957450 · doi:10.1002/acn3.51229

Anakinra usage in febrile infection related epilepsy syndrome: an international cohort

2020· article· en· W3109957450 on OpenAlexaff
Yi‐Chen Lai, Eyal Muscal, Elizabeth Wells, Nikita Shukla, Krista Eschbach, Ki Hyeong Lee, Marios Kaliakatsos, Nevedita Desai, Ronny Wickström, Maurizio Viri, Elena Freri, Tiziana Granata, Srishti Nangia, Robertino Dilena, Andreas Brunklaus, Mark S. Wainwright, Mark Gorman, Coral M. Stredny, Abdurhman Asiri, Khalid Hundallah, Asif Doja, Eric T. Payne, Elaine Wirrell, Sookyong Koh, Jessica L. Carpenter, James J. Riviello

Bibliographic record

VenueAnnals of Clinical and Translational Neurology · 2020
Typearticle
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsAlberta Children's HospitalAgricultural Research Institute of OntarioUniversity of Ottawa
Fundersnot available
KeywordsMedicineAnakinraCohortEpilepsyIntensive care medicineCohort studyPediatricsInternal medicinePsychiatryDisease

Abstract

fetched live from OpenAlex

Febrile-infection related epilepsy syndrome (FIRES) is a devastating neurological condition characterized by a febrile illness preceding new onset refractory status epilepticus (NORSE). Increasing evidence suggests innate immune dysfunction as a potential pathological mechanism. We report an international retrospective cohort of 25 children treated with anakinra, a recombinant interleukin-1 receptor antagonist, as an immunomodulator for FIRES. Anakinra was potentially safe with only one child discontinuing therapy due to infection. Earlier anakinra initiation was associated with shorter duration of mechanical ventilation, ICU and hospital length of stay. Our retrospective data lay the groundwork for prospective consensus-driven cohort studies of anakinra in FIRES.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.161
GPT teacher head0.448
Teacher spread0.287 · 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 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

Citations132
Published2020
Admission routes1
Has abstractyes

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