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Record W3093998529 · doi:10.1097/dbp.0000000000000866

Sleep Variables as Predictors of Treatment Effectiveness and Side Effects of Stimulant Medication in Newly Diagnosed Children with Attention-Deficit/Hyperactivity Disorder

2020· article· en· W3093998529 on OpenAlexafffund
Fiona Davidson, Gabrielle Rigney, Benjamin Rusak, Christine T. Chambers, Malgorzata Rajda, Penny Corkum

Bibliographic record

VenueJournal of Developmental & Behavioral Pediatrics · 2020
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsStimulantSleep (system call)Attention deficit hyperactivity disorderSide effect (computer science)Sleep disorderPsychiatryMedicinePsychologyClinical psychologyInsomnia

Abstract

fetched live from OpenAlex

OBJECTIVE: There is a growing body of research on the impact of stimulant medication on sleep in children with attention-deficit/hyperactivity disorder (ADHD). Negative sleep side effects are a common reason for nonadherence or for discontinuing a course of treatment. However, there is no published evidence as to whether pretreatment sleep can predict responses to treatment and the emergence of side effects. METHOD: In this study, baseline sleep variables were used to predict therapeutic effect (i.e., reduction of ADHD symptoms) and side effects (both sleep and global side effects) in a sample of newly diagnosed, medication-naive children (n = 50). RESULTS: The results of hierarchical regression analysis showed that parent-reported shorter sleep duration before medication treatment significantly predicted better response to treatment, independent of pretreatment ADHD symptoms. Baseline sleep features did not significantly predict global (nonsleep) side effects but did predict increased sleep side effects during treatment. CONCLUSION: These results indicate that baseline sleep variables may be helpful in predicting therapeutic response to medication and sleep disturbance as a side effect of stimulant medication.

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 score0.009
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.285
Teacher spread0.270 · 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

Citations3
Published2020
Admission routes2
Has abstractyes

Explore more

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