Nutritional Status of Autistic Children and Relationship with Nutritional Awareness of Their Mothers
Bibliographic record
Abstract
BACKGROUND: Autism Spectrum Disorder is prevalent worldwide. Autistic children are vulnerable, their preference in food intake, well established, that may lead to abnormal nutrition status, this study designed to describe nutritional awareness of mothers of autistic children.METHODS: This is a descriptive cross-sectional study aimed to determine the nutritional status of autistic children and their mother's awareness in Khartoum state. 67 child aged between 3-18 years were chosen from 12 Centers for children with special needs.Data was collected by questionnaire, which included general information, anthropometric measurements, dietary, and food consumption of children, through case-findings or purposive sampling techniques.RESULTS: The prevalence of autism is higher in males than females, males (77.6%), females (22.4%). Socio-economic status findings showed (65.7%) were from middle class income. Autistic children fathers (74.6%) employed and employed mothers were (28.3%). Nutritional status showed Preschool Females (100%) underweight; while school age males were (44.4%). 94.9% of them consume wheat and other cereal products. Frequent attacks of upper respiratory tract infection occurs in (97%), malaria and worms infestation occurred in (49.3%) and (46.3%) had teeth decay.CONCLUSION & RECOMMENDATIONS: Nutrition awareness is essential for mothers of autistic children. It’s recommended that a dietitian should be a member in each center to educate mothers about consumption of recommended food especially in the early childhood for a better outcome through adulthood.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".