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Record W2552468093 · doi:10.1002/jaba.351

Establishing motion control in children with autism and intellectual disability: Applications for anatomical and functional MRI

2016· article· en· W2552468093 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Behavior Analysis · 2016
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsSt.AmantUniversity of Manitoba
Fundersnot available
KeywordsAutismMagnetic resonance imagingPsychologyAutism spectrum disorderMotion (physics)Psychological interventionPhysical medicine and rehabilitationAudiologyMedicineDevelopmental psychologyPsychiatryRadiologyArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Excessive motion makes magnetic resonance imaging (MRI) extremely challenging among children with autism spectrum disorder (ASD). The medical risks of sedation establish the need for behavioral interventions to promote motion control among children with ASD undergoing MRI scans. We present a series of experiments aimed at establishing both tolerance of the MRI environment and a level of motion control that would be compatible with a successful MRI. During Study 1, we evaluated the effects of prompting and contingent reinforcement on compliance with a sequence of successive approximations to an MRI using a mock MRI. During Study 2, we used prompting and progressive differential reinforcement of other behaviors (DRO) to promote motion control in a mock MRI for increasing periods of time. Finally, during Study 3, some of the participants underwent a real MRI scan while a detailed in-session motion analysis informed the quality of the images captured.

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.

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 score0.175
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.272
Teacher spread0.255 · 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