Notice bibliographique
Résumé
AFTER AN ARTICLE THAT DISCOURagesroutineautismscreeningappearedonline inPediatrics, coauthor JanWillemGorter,MD,PhD,heard an array of heartfelt responses. “Some peoplewereupset,especiallyparentswith a child with autism,” said Gorter, an associate professor of pediatrics in the McMaster University Faculty of Health Sciences in Hamilton, Ontario, Canada. “I also got responses of parents who had a child mislabeled with autism initially, and that had a huge impact on the parents’ life and the child’s life.” The study struck an emotional chord by concluding that sound evidence to support routine screening is lacking (AlQabandi M et al. Pediatrics. 2011;128[1]: e211-e217). The authors say that currently available autism screening tools have not been evaluated in randomized controlled trials and that treatment is only modestly effective in certain subgroups of children. Although childhood screening, early detection, and treatmentareeffective for conditions such as congenital hypothyroidism or phenylketonuria, Gorter and his colleagues say that existing evidence doesnot showthat routine screening for autismdoesmoregoodthanharm.Infact, theysaythatmisdiagnosescanstigmatize children, expose them to unnecessary treatment, and trigger excessive costs. Gorter noted that the article focuses on routine screening that includes apparently healthy children, not clinical surveillance in which pediatricians evaluate children because they or the parents suspect a problem. The data were taken from a literature search designed to answer 7 questions concerning the appropriateness, feasibility, and value of screening for autism. In 2007, the American Academy of Pediatrics (AAP) published a clinical report that supports screening all children, regardless of risk factors, for autism spectrum disorders beginning at age 18 months (Johnson CP et al. Pediatrics. 2007;120[5]:1183-1215). For children without risk factors, the AAP report says appropriate evaluation methods include several screening tools that consist of parental interviews, questionnaires, or direct observation of the child. Among them are the Checklist for Autism in Toddlers (CHAT) and the Modified Checklist for Autism in Toddlers (M-CHAT). Both are available at no cost to primary care pediatricians. The reported specificity of CHAT is at least 98%, but sensitivity is between 18% and 38%. M-CHAT has 93% specificity and 85% sensitivity. Gorter and his colleagues say that M-CHAT, a 23item questionnaire that takes parents about 5 minutes to complete, is a “promising” tool, but that it misses 15 of every 100 children with autism. They say that none of the currently available screening tests “fulfill the properties of accuracy, namely high sensitivity, high specificity, and high predictive value” in population-based screening programs. Gorter said he and his colleagues did not intend for their study to split clinicians into “right” and “wrong” camps. “We took a scientific approach,” he said. “We wanted to consider not only what we know about testing itself, but also the impact of the screening program on public health and society at large.” Catherine Lord, PhD, director of the UniversityofMichiganAutismandCommunication Disorders Center in Ann Arbor, said the study lacks a necessary ingredient: a thoroughcost-benefit analysis. “We need to know what is the cost of screening and the benefit of screening, and what is the cost of treatment and the benefit of treatment,” said Lord, who was not involved in Gorter’s research. “It’s certainly not true that we can cure autism, but to say that nobody has ever shown a treatment has any effectiveness, any generalizability, that’s just not true.” Fred R. Volkmar, MD, director of the Child Study Center at the Yale University School of Medicine, agreed that interventions for autism do make a differ-
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 | 0,061 | 0,213 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,011 |
| Communication savante | 0,008 | 0,017 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,018 | 0,039 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,004 |
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 ».