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Record W2789431015 · doi:10.1177/1087054718763878

Sensitivity and Specificity of an Executive Function Screener at Identifying Children With ADHD and Reading Disability

2018· article· en· W2789431015 on OpenAlexaff
Justin E. Karr, Michelle Y. Kibby, Audreyana Jagger‐Rickels, Mauricio A. García-Barrera

Bibliographic record

VenueJournal of Attention Disorders · 2018
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of Victoria
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsPsychologyExecutive functionsReading (process)Reading disabilityClinical psychologyDevelopmental psychologyCognitive psychologyDyslexiaCognitionPsychiatryLinguistics

Abstract

fetched live from OpenAlex

Objective: This study evaluated the sensitivity/specificity of a global sum score (GSS) from the Behavior Assessment System for Children, Second Edition, Executive Function screener (BASC-2-EF) at classifying children with/without ADHD and/or reading disability (RD). Method: The BASC-2 Teacher/Parent Rating Scales (TRS/PRS) were completed for children (8-12 years old; 43.1% female) with no diagnosis ( n = 53), RD ( n = 34), ADHD ( n = 85), co-morbid RD/ADHD ( n = 36), and other diagnoses ( n = 15). Receiver operating characteristic (ROC) curve analyses evaluated the sensitivity/specificity of the BASC-2-EF GSS at discriminating between children with/without ADHD or RD. Results: Area under the curve (AUC) scores indicated the sensitivity/specificity of the BASC-2-EF GSS at discriminating between children with/without ADHD (TRS: AUC = .831, p < .001; PRS: AUC = .919, p < .001), with/without RD (TRS: AUC = .724, p = .001; PRS: AUC = .615, p = .101), and with ADHD or RD through post hoc analysis (TRS: AUC = .674, p = .006; PRS: AUC = .819, p < .001). Conclusion: The findings support utilizing the BASC-2-EF GSS when differentiating ADHD from RD and typical development.

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 score0.024
Threshold uncertainty score0.565

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.303
Teacher spread0.274 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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