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Record W3015104497 · doi:10.5539/jmbr.v10n1p59

Investigation of the Relationship between Parental Mental Disorders and Autism among the Children of West Azerbaijan -Iran

2020· article· en· W3015104497 on OpenAlexvenueno aff
Arezou Kiani Equal, Javad Rasouli, Sahar Kiani

Bibliographic record

VenueJournal of Molecular Biology Research · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
Fundersnot available
KeywordsAutismAnxietyPsychiatryClinical psychologyPsychologyHostilityDepression (economics)Mental healthMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.099
GPT teacher head0.364
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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