Investigation of the Relationship between Parental Mental Disorders and Autism among the Children of West Azerbaijan -Iran
Bibliographic record
Abstract
Background: Autism disorders have increased over the last years. Autism is a neurological growth disorder associated with social communication disorders, growth retardation, and repetitive behaviors, along with serious consequences for children and families. The purpose of this research was to evaluate the relationship between parental mental disorders and autism among the children of West Azerbaijan Province. Methods: This research was a case-control study in which the case group subjects were selected among the parents with autistic children and control group subjects were selected among the relative parents with healthy children and non-relative parents with healthy children. Both case and control groups were matched in terms of gender, living place, and age of children. Finally, the data were analyzed using SPSS-16 (Chicago, IL, USA) software. Results: The current research results revealed that the frequency of mental diseases, including obsessive-compulsive, inter personality sensitivity, depression, anxiety, hostility, phobia, paranoid ideation, and psychotic disorder are different in fathers and mothers of the case and control groups. As Pvalue was lower than 0.05 in all scales, there was a significant relationship between the mental diseases of parents and the history of mental disorders in relatives and autism. Conclusion: The prevalence of mental disorders in relatives and having a medical history can be a warning sign of autism in children.
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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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".