Simultaneous Observational Study of Socio-Demographic, Ante- and Intranatal Risk Factors for Mild Cognitive Impairment and ADHD in Children
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".