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Record W2145187548 · doi:10.1155/2011/541926

Association of Sensory Processing and Eating Problems in Children with Autism Spectrum Disorders

2011· article· en· W2145187548 on OpenAlexafffund
Geneviève Nadon, Debbie Ehrmann Feldman, Winnie Dunn, Erika G. Gisel

Bibliographic record

VenueAutism Research and Treatment · 2011
Typearticle
Languageen
FieldMedicine
TopicChild Nutrition and Feeding Issues
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in RehabilitationUniversité de Montréal
FundersRéseau Provincial de Recherche en Adaptation-RéadaptationCentre for Interdisciplinary Research in Rehabilitation
KeywordsSensory systemAutismSensory processingAutism spectrum disorderAssociation (psychology)AudiologyTypically developingPsychological interventionEating disordersMedicineClinical psychologyPsychologyPsychiatryNeuroscience

Abstract

fetched live from OpenAlex

"Selective" or "picky eating" is a frequent problem in children with autism spectrum disorders (ASD). Many of these children do not treat sensory input, particularly olfactory, auditory, visual, and tactile information in the same manner as their typically developing peers of the same age. The purpose of this paper was to examine the relationship between problems of sensory processing and the number of eating problems in children with ASD. Of 95 children with ASD, 3 to 10 years of age, 65 percent showed a definite difference and 21 percent a probable difference in sensory processing on the total score of the Short Sensory Profile. These results were significantly related to an increase in the number of eating problems measured by the Eating Profile. These results could not be explained by age, sex, mental retardation, attention deficit disorder, or hyperactivity. Timely interventions focusing on the sensory components of eating must now be developed.

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)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.048
GPT teacher head0.310
Teacher spread0.261 · 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

Citations189
Published2011
Admission routes2
Has abstractyes

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