Concordance of Diagnosis of Autism Spectrum Disorder Made by Pediatricians vs a Multidisciplinary Specialist Team
Notice bibliographique
Résumé
Importance: Wait times for autism spectrum disorder (ASD) diagnosis are lengthy because of inadequate supply of specialist teams. General pediatricians may be able to diagnose some cases of ASD, thereby reducing wait times. Objective: To determine the accuracy of ASD diagnostic assessments conducted by general pediatricians compared with a multidisciplinary team (MDT). Design, Setting, and Participants: This prospective diagnostic study was conducted in and a specialist assessment center in Toronto, Ontario, Canada, and Ontario general pediatrician practices from June 2016 to March 2020. Children were younger than 5.5 years, referred with a developmental concern, and without an existing ASD diagnosis. Data analysis was performed from October 2021 to February 2022. Exposures: The pediatrician and MDT each conducted blinded assessments and recorded a decision as to whether the child had ASD. Main Outcomes and Measures: Main outcomes included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A logistic regression was performed to identify factors associated with accurate pediatrician assessment for children with or without an ASD diagnosis. Results: Seventeen pediatricians (12 women [71%]) participated in the study and referred 106 children (79 boys [75%]; mean [SD] age, 41.9 [13.3] months). Sixty participants (57%) were from minoritized racial and ethnic groups (eg, Black, Asian, Hispanic, Middle Eastern, and multiracial). Seventy-two participants (68%) received a diagnosis of ASD by the MDT. Sensitivity and specificity of the pediatrician assessments compared with MDT were 0.75 (95% CI, 0.67-0.83) and 0.79 (95% CI, 0.62-0.91), respectively. The PPV of the pediatrician assessments was 0.89 (95% CI, 0.80-0.94) (ie, 89% agreement with the MDT), and NPV was 0.60 (95% CI, 0.49-0.70) (ie, 60% agreement with the MDT). Higher pediatrician certainty (odds ratio [OR], 3.33; 95% CI, 1.71-7.34; P = .001) was associated with increased diagnostic accuracy for children with ASD. Lower accuracy was seen for children with higher Visual Reception subscale developmental skills (OR, 0.93; 95% CI, 0.89-0.97; P = .001), speaking abilities (OR, 0.17; 95% CI, 0.03-0.67; P = .03), and White race (OR, 0.32; 95% CI, 0.10-0.97; P = .04). Age, gender, and Autism Diagnostic Observation Schedule, 2nd Edition composite scores were not significantly associated with the accuracy of assessments. All 7 children with a sibling with ASD received an accurate diagnosis; otherwise, no significant factors were identified for accuracy in children without ASD. Conclusions and Relevance: This study of concordance of autism assessment between pediatricians and an expert MDT in young children found high accuracy when general pediatricians felt confident and lower accuracy when ruling out ASD. These findings suggest that children with co-occurring delays may be potential candidates for community assessment.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».