Simultaneous Observational Study of Socio-Demographic, Ante- and Intranatal Risk Factors for Mild Cognitive Impairment and ADHD in Children
Notice bibliographique
Résumé
Justification. Mild cognitive impairment in children remains an insufficiently studied problem in terms of etiopathogenetic aspects and the identification of risk factors. This is largely due to the borderline interdisciplinary position of this area of clinical interest. Among other problems, the lack of understanding of the origin of these conditions leads to the stagnation of diagnostic and therapeutic tactics and strategies, in particular, there are no provisions for the early diagnosis of these disorders. The aim of the study was to identify socio-demographic, ante-, intranatal, and early neonatal predictors of disorders from the group of mild cognitive impairment and attention deficit hyperactivity disorder in children. Methods. The study included children aged 5–12 years with mild cognitive impairment (MCI), attention deficit hyperactivity disorder (ADHD), as well as neurologically healthy children (control group) living in the Moscow agglomeration, Ekaterinburg, Chelyabinsk and Irkutsk. The criteria for inclusion in the groups were determined based on data from neurological examinations and specialized neuropsychological and speech therapy testing. The parents of the children included in the study filled out a specially designed electronic questionnaire of 47 items, covering socio-demographic characteristics, conditions and circumstances of pregnancy, childbirth and the early neonatal period. The differences in the frequency of distribution of the studied signs between the pathology groups (MCI, ADHD) and the control group (neurologically healthy children) were evaluated, the odds ratio of having MCI depending on the presence of a predictor and the prognostic characteristics of the model of a combination of several predictors were determined. Results. The study included 344 children, of whom 190 children were in the control group, and 154 children were in the main clinical groups. 120 children were in the LCN group, 72 children in the ADHD group, and 36 more in the comorbid MCI and ADHD group. In MCI, ADHD was observed in 31.7 % of cases. A total of 18 different predictors of LVH were identified, of which the most significant were the need for ventilation (odds ratio OR = 22.59; CI: 2.76–185.06), frequent/copious regurgitation (OR = 9.49; CI: 2.04–44.18) and symptoms of neurological well-being in general (OR = 5.60; CI: 2.91–10.76) in the early neonatal period. The leading predictors were similar for MCI and ADHD. Predictive models of combinations of the most significant predictors are able to correctly predict 92.5 % and 87.5 % of outcomes between MCI and the neurological norm. Conclusion. There is no doubt that perinatal events affecting the brain, in addition to genetic determinations, should be considered as a risk factor and an etiological factor of MCI and ADHD. This raises the question of the need to identify risk groups for early diagnosis of pathology and the earliest possible treatment of these conditions. The predictors resulting from the results of the study can be used separately and in combination for these purposes. The results of the study are pushing for a revision of the official positions on the management of children with MCI. MCI and ADHD are closely related conditions, which must be taken into account in therapeutic approaches to the management of these disorders.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 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 ».