Commentary: Effects of diagnostic thresholds and research vs service and administrative diagnosis on autism prevalence
Notice bibliographique
Résumé
King and Bearman are to be congratulated on their sophisticated analysis of the Californian Department of Developmental Services (DDS) database. In contrast with previous attempts to examine diagnostic substitution and diagnostic accretion (both in the same data source and in national administrative data sources), which allowed time trends but not individual child-level diagnostic substitutions and accretions to be examined, they demonstrated that children previously classified with ‘mental retardation’ account for one-quarter of the measured increase in autism prevalence in the DDS. However, King and Bearman highlight the fact that this leaves nearly three-quarters of the increase to be explained by other factors. The information available in administrative databases such as the DDS do not allow for any test of what these ‘other factors’ might be. Thus, their analysis does not answer the ‘great questions’ that have engaged both the scientific community and the general public: has there been a real increase in incidence and, if so, why? What has been the impact of changes in diagnostic practice, public and professional awareness of autism and other methodological factors (e.g. broadening of our concept of autism, different methods of ascertainment, inclusion of individuals with average IQ and those with other neuropsychiatric and medical disorders) that likely account for much of these dramatic time trends? Service administration databases are not prevalence studies and changes in recorded need might reflect changes in entitlement or availability of particular services, rather than true changes in prevalence. However, over the past 10 years epidemiological studies have also found much higher rates of autism than previously; so the administrative increase identified in the DDS data has also been reflected in the findings from prevalence studies. For several decades following Lotter’s seminal study, the autism prevalence figure that he reported of 4.5 cases per 10 000 was broadly accepted, although 20 per 10 000 children was noted as showing the broader ‘triad of impairments’ characterized by Wing and Gould. Over the past decade, prevalence rates for autism in the range of between 20 and 40 per 10 000 have been reported. The rates for the broader autism spectrum have ranged from 60 per 10 000 to close to and even over 100 per 10 000. However, quantifying the effects of the various methodological factors that might help explain the higher figures found in more recent studies is difficult. Williams et al. conducted a systematic review of autism prevalence studies and found that diagnostic criteria [higher prevalence in studies using International Classification of Diseases (ICD-10) or Diagnostic and Statistical Manual of Mental Disorders (DSM-IV)], age of the sample (higher prevalence in younger samples) and study location (higher prevalence in Japanese vs US studies), are systematically related to the prevalence figures reported across 37 studies. A significant proportion (61%) in the variance in prevalence rates across studies could be explained by these factors. However, Williams et al. concluded that the three factors identified might be acting as a proxy for other study characteristics that affect prevalence rate. In a univariate analysis, they found that decade of study had the largest
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,172 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,012 | 0,003 |
| Intégrité de la recherche | 0,060 | 0,043 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,018 | 0,011 |
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 source (Gemma direct ou Codex distillé), 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 ».