Measures of gluten-related reactivity in children with autism spectrum disorders in the absence of overt gastrointestinal symptoms: a pilot study from the United Arab Emirates
Bibliographic record
Abstract
OBJECTIVES: The aetiology of autism spectrum disorder (ASD) is multifactorial, sometimes genetic, and may be associated with abnormal immunological responses to peptides from proteins such as gluten. These peptides may cross the blood-brain barrier and affect neurotransmission, resulting in behavioural symptoms consistent with ASD. The aim of this study was to screen for markers of gluten-related immune reactivity in the absence of overt gastrointestinal symptoms in patients with ASD in the United Arab Emirates, a country associated with a high prevalence of ASD but lacking this type of research. METHODS: Patients diagnosed with ASD (using Diagnostic and Statistical Manual of Mental Disorders-IV-based criteria and Autism Diagnostic Observational Schedules) were compared with controls, regarding anti-tissue transglutaminase (tTG) immunoglobulin (Ig) A and anti-deamidated gliadin peptide (DGP) IgA levels. RESULTS: Sixty-six patients with ASD and 101 controls were included. Patients with ASD showed statistically significant lower anti-DGP IgA levels, but no significant difference in anti-tTG IgA levels, versus healthy controls. Correlations between immunological data and clinical symptoms were synergistic, but not statistically significant. CONCLUSION: ASD may be associated with reduced levels of anti-DGP IgA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".