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Record W4283819540 · doi:10.31234/osf.io/uzvm7

Sensory Processing Patterns Predict Problem Behaviours in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder

2022· preprint· en· W4283819540 on OpenAlexaboutno aff
Samantha Schulz, Elizabeth Kelley, Evdokia Anagnostou, Rob Nicolson, Stelios Georgiades, Jennifer Crosbie, Russell Schachar, Muhammad Ayab, Ryan A. Stevenson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsSensory processingSensory systemAutism spectrum disorderPsychologyAutismAssociation (psychology)AudiologyCognitive psychologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Objectives: Sensory processing is the ability to discern and understand information from one’s sensory organs. Understanding sensory processing patterns in different clinical groups could elicit evidence that sensory processing patterns are a transdiagnostic mechanism that is important to understand in neurodevelopmental disorders. Furthermore, there is little evidence of how sensory processing patterns relate to behaviours, such as attention, social, and mood difficulties in autism and ADHD. The goals of this study were to directly compare sensory processing patterns in autism, ADHD, and typical development and to explore the association between sensory processing and behavioural outcomes. Methods: Data were collected through the Province of Ontario Neurodevelopmental Network. The parents of 805 children with typical development, ADHD, or ASD completed measures of sensory processing and behavioural outcomes with the Short Sensory Profile and the Childhood Behaviour Checklist, respectively. Sensory processing was compared across groups and regression analyses were conducted to determine if behavioral patterns could be predicted by sensory processing patterns in the clinical sample. Results: Overall, the results identified significant differences in sensory processing patterns between the diagnosed and undiagnosed participants. Autism and ADHD differed on all Sensory Profile subscales except Auditory Filtering and Under-Responsivity/Sensory Seeking. All behavioural outcomes were predicted by sensory processing patterns over and above the variance accounted for by diagnostic group, suggesting that understanding sensory processing patterns is an important piece of a comprehensive understanding of the behavioural patterns observed across multiple clinical populations. Conclusions: There is evidence that sensory processing is different in ASD and ADHD but that specific patterns of sensory processing are related to behavioural outcomes in both disorders. Better understanding sensory processing as a mechanism for behaviour can help to identify simple interventions across neurodevelopmental disorders.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.124
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.307
Teacher spread0.277 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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