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Record W2126310660 · doi:10.1177/1088357615588517

Preliminary Randomized Trial of Function-Based Cognitive-Behavioral Therapy to Treat Obsessive Compulsive Behavior in Children With Autism Spectrum Disorder

2015· article· en· W2126310660 on OpenAlexaff
Tricia Vause, Nicole Neil, Heather Jaksic, Grazyna Jackiewicz, Maurice A. Feldman

Bibliographic record

VenueFocus on Autism and Other Developmental Disabilities · 2015
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsLaurentian UniversityBrock University
FundersYale University
KeywordsPsychoeducationAutism spectrum disorderRandomized controlled trialPsychologyCognitive behavioral therapyClinical psychologyAutismCognitionIntervention (counseling)Psychological interventionCognitive restructuringPsychiatryMedicine

Abstract

fetched live from OpenAlex

Individuals with high functioning autism spectrum disorder (ASD) frequently experience obsessions and/or compulsions that are similar to those specified in Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) criteria for obsessive-compulsive disorder (OCD). However, little research exists on effective interventions for OCD-like behaviors (referred to as OCBs) in ASD. In a preliminary randomized controlled trial (RCT; N = 14), a manualized function-based cognitive-behavior therapy (Fb-CBT) consisting of traditional CBT components (psychoeducation and mapping, cognitive-behavioral skills training, exposure, and response prevention) as well as function-based behavioral assessment and intervention significantly decreased OCBs in 8- to 12-year-old children with ASD at post-treatment and 5-month follow-up. This multi-component treatment shows considerable promise, and a larger RCT is needed to further validate and expand these findings.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.044
GPT teacher head0.305
Teacher spread0.262 · 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 designRandomized trial
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

Citations31
Published2015
Admission routes1
Has abstractyes

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Same venueFocus on Autism and Other Developmental DisabilitiesSame topicAutism Spectrum Disorder ResearchFrench-language works237,207