MétaCan
Menu
Back to cohort

Obstructive Sleep Apnea in Pediatric Neuroinflammatory Conditions (P4.2-098)

2019· article· en· W3205531971 on OpenAlexaff
Michelle J. Jaeggi, Stephanie A. Grover, Tara Berenbaum, Samantha Stephens, Shelly K. Weiss, Eric Yeh

Bibliographic record

VenueNeurology · 2019
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick ChildrenMental Health Research Canada
Fundersnot available
KeywordsMedicineObstructive sleep apneaSleep apneaSleep (system call)ApneaPediatricsInternal medicineComputer science

Abstract

fetched live from OpenAlex

To establish the frequency of obstructive sleep apnea (OSA) in children with monophasic acquired demyelinating syndrome (Mono-ADS), multiple sclerosis (MS), and other recurrent neuroinflammatory conditions including neuromyelitis optica spectrum disorder (NMOSD), myelin oligodendrocyte glycoprotein antibody positive (MOG+), and recurrent optic neuritis (ON).

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)0.000
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 score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueNeurologySame topicAutoimmune and Inflammatory Disorders ResearchFrench-language works237,207