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Enregistrement W3044006808 · doi:10.1177/1362361320942091

Vision care among school-aged children with autism spectrum disorder in North America: Findings from the Autism Treatment Network Registry Call-Back Study

2020· article· en· W3044006808 sur OpenAlexaboutno aff
Olivia J. Lindly, James Chan, Rachel M. Fenning, Justin G. Farmer, Ann M. Neumeyer, Paul P. Wang, Mark W. Swanson, Robert A. Parker, Karen Kuhlthau

Notice bibliographique

RevueAutism · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueOphthalmology and Visual Impairment Studies
Établissements canadiensnon disponible
Organismes subventionnairesAgency for Healthcare Research and QualityMassachusetts General Hospital
Mots-clésAutism spectrum disorderAutismPsychologyPsychiatryMedicineClinical psychology

Résumé

récupéré en direct d'OpenAlex

Children with autism spectrum disorder have a high risk of vision problems yet little is known about their vision care. This cross-sectional survey study, therefore, examined vision care among 351 children with autism spectrum disorder ages 6–17 years in the United States or Canada who were enrolled in the Autism Treatment Network Registry. Vision care variables were vision tested with pictures, shapes, or letters in the past 2 years; vision tested by an eye care practitioner (e.g. ophthalmologist, optometrist) in the past 2 years; prescribed corrective eyeglasses; and wore eyeglasses as recommended. Covariates included sociodemographic, child functioning, and family functioning variables. Multivariable models were fit for each vision care variable. Though 78% of children with autism spectrum disorder had their vision tested, only 57% had an eye care practitioner test their vision in the past 2 years. Among the 30% of children with autism spectrum disorder prescribed corrective eyeglasses, 78% wore their eyeglasses as recommended. Multivariable analysis results demonstrated statistically significant differences in vision care among children with autism spectrum disorder by parent education, household income, communication abilities, intellectual functioning, and caregiver strain. Overall, study results suggest many school-aged children with autism spectrum disorder do not receive recommended vision care and highlight potentially modifiable disparities in vision care. Lay Abstract Children with autism are at high risk for vision problems, which may compound core social and behavioral symptoms if untreated. Despite recommendations for school-aged children with autism to receive routine vision testing by an eye care practitioner (ophthalmologist or optometrist), little is known about their vision care. This study, therefore, examined vision care among 351 children with autism ages 6–17 years in the United States or Canada who were enrolled in the Autism Treatment Network Registry. Parents were surveyed using the following vision care measures: (1) child’s vision was tested with pictures, shapes, or letters in the past 2 years; (2) child’s vision was tested by an eye care practitioner in the past 2 years; (3) child was prescribed corrective eyeglasses; and (4) child wore eyeglasses as recommended. Sociodemographic characteristics such as parent education level, child functioning characteristics such as child communication abilities, and family functioning characteristics such as caregiver strain were also assessed in relationship to vision care. Although 78% of children with autism had their vision tested, only 57% had an eye care practitioner test their vision in the past 2 years. Among the 30% of children with autism prescribed corrective eyeglasses, 78% wore their eyeglasses as recommended. Differences in vision care were additionally found among children with autism by parent education, household income, communication abilities, intellectual functioning, and caregiver strain. Overall, study results suggest many school-aged children with autism do not receive recommended vision care and highlight potentially modifiable disparities in vision care.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,249
Score d'incertitude au seuil0,495

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,284
Écart entre enseignants0,270 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations17
Publié2020
Routes d'admission1
Résumé présentoui

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