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Record W3148431844 · doi:10.3389/fpsyt.2021.638901

Developing Consensus in the Assessment and Treatment Pathways for Autoimmune Encephalitis in Child and Adolescent Psychiatry

2021· article· en· W3148431844 on OpenAlexafffundabout
GenaLynne C. Mooneyham, Vladimir Ferrafiat, Erin Stolte, Dietmar Fuchs, Doron Cohen

Bibliographic record

VenueFrontiers in Psychiatry · 2021
Typearticle
Languageen
FieldMedicine
TopicAutoimmune Neurological Disorders and Treatments
Canadian institutionsUniversity of Alberta
FundersNational Institutes of HealthUniversity of AlbertaVanderbilt UniversityAmerican Academy of Child and Adolescent Psychiatry
KeywordsAutoimmune encephalitisMedicineChild and adolescent psychiatryNeurologyPsychiatryCatatoniaEncephalitisIntensive care medicineImmunologySchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

Children with a diagnosis of Autoimmune Encephalitis (AE) frequently require multi-disciplinary care in order to mobilize the assessment and treatment necessary for recovery. Institutional and provider practice differences often influence the diagnostic workup and treatment pathways made available to patients. There are a variety of provider coalitions in pediatric rheumatology, internal medicine, and neurology that have been making meaningful progress toward the development of consensus in assessment and treatment approaches to patient care. However, child psychiatry is currently underrepresented in this work in spite of the high psychiatric symptom burden seen in some young patients. The need for consensus is often made visible only with inter-institutional dialogue regarding patient care trajectories. We aim to review key updates in the assessment and treatment of children and adolescents with autoimmune encephalitis during the acute phase, with or without catatonia, and to outline provider perspectives by comparing current treatment models in the United States, Canada, and Europe.

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 score0.047
Threshold uncertainty score0.716

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)0.0000.000

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 head0.024
GPT teacher head0.293
Teacher spread0.269 · 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

Citations8
Published2021
Admission routes3
Has abstractyes

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