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Record W2572550024 · doi:10.3899/jrheum.160556

Sleep Disturbances and Neurobehavioral Performance in Juvenile Idiopathic Arthritis

2017· article· en· W2572550024 on OpenAlexvenueno aff
Teresa M. Ward, Dean W. Beebe, Maida Lynn Chen, Carol A. Landis, Sarah Ringold, Ken Pike, Carol A. Wallace

Bibliographic record

VenueThe Journal of Rheumatology · 2017
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsnot available
FundersNational Institute of Nursing Research
KeywordsObstructive sleep apneaMedicineHypopneaExcessive daytime sleepinessApneaPhysical therapyAudiologyAnesthesiaPolysomnographyPsychologySleep disorderInsomniaPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the extent of polysomnographic (PSG) sleep disturbances [obstructive apnea hypopnea index (OAHI), number of wake bouts, arousals, periodic limb movements] and the effect of OAHI on neurobehavioral performance in juvenile idiopathic arthritis (JIA) with obstructive sleep apnea (OSA), JIA without OSA, and controls without OSA, adjusting for intelligence quotient (IQ), pain, medications, daytime sleepiness, and wake bouts. METHODS: Children 6-11 years, 68 with JIA and 67 controls, underwent 1 night of PSG and completed self-reported daytime sleepiness surveys, multiple sleep latency tests for physiological sleepiness, and neurobehavioral performance tests the next day. RESULTS: Compared with JIA and controls without OSA, mean OAHI and arousals were significantly higher in JIA with OSA (p < 0.001, respectively). In comparison with JIA and controls without OSA, mean simple reaction time and sustained attention were significantly slower in JIA with OSA, adjusting for IQ, pain, any medication, daytime sleepiness, and wake bouts. CONCLUSION: Elevated OAHI is suggestive of obstructive sleep apnea and a comorbidity in JIA that may predispose children with JIA to daytime sleepiness and impaired neurobehavioral performance.

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)0.001
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 score0.039
Threshold uncertainty score0.203

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.287
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 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

Citations13
Published2017
Admission routes1
Has abstractyes

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