A.02 Clinical clues for autoimmunity in the etiology of autistic regression
Bibliographic record
Abstract
Background: Autistic regression (AR) accounts for 20-40% of patients with Autism Spectrum Disorder (ASD) .1 Literature demonstrates specific immune changes in AR patients,2 as well as association between AR and autoimmune thyroiditis.3 Our study explores the clinical association between AR and autoimmunity, focusing on possible precipitants and familial autoimmunity, in comparison with patients with infantile autism (IA). Methods: charts of children diagnosed with ASD in 2014 were reviewed, and patients were classified as either AR or IA based on Autism Diagnostic Interview (ADI-R) criteria.4 Information regarding pregnancy, perinatal complications, febrile illness preceding the diagnosis, and family history of autoimmune conditions was collected. Results: 206 children had IA and 33 had AR. No difference was found in prevalence of pregnancy or perinatal complications. The incidence of febrile illness in the 6 months prior to diagnosis and the prevalence of familial autoimmunity, were significantly higher in the AR group (p<0.001). Diabetes type I, celiac disease, autoimmune thyroiditis, and inflammatory bowel disease were more common in families of AR patients (p<0.05). Conclusions: the association between AR and preceding febrile illness, as well as familial autoimmunity, supports the notion of AR as a separate entity within ASD, possibly mediated by autoimmune changes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".