Dietary diversity and nutritional status of children with and without autism spectrum disorder: a comparative cross-sectional study in Bangladesh
Bibliographic record
Abstract
The objective of our study was to compare the dietary diversity and nutritional status of children with autism spectrum disorders (ASD) and non-ASD. We included a total of 344 children in this cross-sectional study; of them,172 non-ASD children from three public schools and 172 ASD children from six special schools in Dhaka, Bangladesh. We used a multinomial logistic regression model to assess the association of ASD with nutritional status and dietary diversity among children. The mean age of the children was 7.9 years; 29.7% were female. ASD children were more likely to be overweight and obese compared to the non-ASD group (RRR: 2.85, 95% CI 1.28–6.34, p-value 0.011). ASD children had lower dietary diversity than non-ASD children (RRR: 18.57, 95% CI 4.49–76.77, p-value < 0.001). Children with ASD had a significantly lower daily frequency of intake of food from starchy roots, sugars, preserves and syrups, meat, fish and eggs, milk and milk products than non-ASD children (p-value < 0.05). However, consumption of cereals, vegetables and fruits, fats and oils, and beverages was almost similar for both groups of children. ASD children had a higher risk of being overweight and obese and a lower dietary diversity. We recommend robust longitudinal studies to explore the role of tailored intervention approaches for dietary behavior modification and weight management.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".