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Record W3160195066 · doi:10.31234/osf.io/myjbq

Sensory Processing in ASD and ADHD: A Confirmatory Factor Analysis

2020· preprint· en· W3160195066 on OpenAlexaff
Kaitlyn Mary Ann Parks, Samantha Schulz, Christina G. McDonnell, Evdokia Anagnostou, Rob Nicolson, Elizabeth A. Kelley, Stelios Georgiades, Jennifer Crosbie, Russell Schachar, Xudong Liu, Ryan A. Stevenson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsHospital for Sick ChildrenMcMaster UniversityQueen's UniversityHolland Bloorview Kids Rehabilitation HospitalWestern University
Fundersnot available
KeywordsSensory processingAutism spectrum disorderSensory systemPsychologyStimulus modalityConfirmatory factor analysisAttention deficit hyperactivity disorderAutismAudiologyCognitive psychologyDevelopmental psychologyClinical psychologyStructural equation modelingMedicineMachine learningComputer science

Abstract

fetched live from OpenAlex

Difficulties related to sensory processing in Autism Spectrum Disorder (ASD) and attention/deficit-hyperactivity disorder (ADHD) can manifest within multiple modalities and impact daily functioning. We examined the factor structure of the Short Sensory Profile in ASD and ADHD (N=517). A seven-factor structure held for ASD and ADHD however, differences were found in responses to the sensory measure. At the subscale level, a two-factor structure emerged in ASD, while a single-factor solution emerged in ADHD. Although there are differences in the way those with ASD and ADHD process sensory information, a single inventory can be used to describe processing patterns in both groups. Establishing the shared and non-shared features of sensory processing may have the potential to inform more targeted clinical approaches.

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)0.009
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.116
GPT teacher head0.375
Teacher spread0.260 · 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 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

Citations5
Published2020
Admission routes1
Has abstractyes

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