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Optimizing the Diagnosis and Management of Dravet Syndrome: Recommendations From a North American Consensus Panel

2017· article· en· W2583598024 on OpenAlexaff
Elaine Wirrell, Linda Laux, Elizabeth Donner, Nathalie Jetté, Kelly G. Knupp, Mary Anne Meskis, Ian Miller, Joseph Sullivan, Michelle Welborn, Anne T. Berg

Bibliographic record

VenuePediatric Neurology · 2017
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsHotchkiss Brain InstituteUniversity of CalgaryUniversity of Toronto
FundersDravet Syndrome Foundation
KeywordsDravet syndromeMedicineLennox–Gastaut syndromeEpilepsy syndromesIntensive care medicinePediatricsEpilepsyPsychiatryFamily medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.042
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.077
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0060.004
Science and technology studies0.0030.002
Scholarly communication0.0050.005
Open science0.0090.004
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0040.002

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 head0.055
GPT teacher head0.322
Teacher spread0.267 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations283
Published2017
Admission routes1
Has abstractno

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