Clinical clues for autoimmunity and neuroinflammation in patients with autistic regression
Bibliographic record
Abstract
AIM: Autistic regression is a unique variant within the autism spectrum disorders (ASDs), with recent reports raising the possibility of immune aetiology. This study explores clinical clues for an association between autistic regression and autoimmunity. METHOD: Single-centre charts of children diagnosed with ASD in 2014 were reviewed. We compared the rates of: (1) familial autoimmunity in first-degree and second-degree relatives; (2) febrile illness preceding initial parental concern, as a potential precipitant of immune activation; and (3) possible non-immune precipitants such as pregnancy and postnatal complications. RESULTS: The charts of 206 children with ASD and 33 diagnosed with autistic regression variant were reviewed. The incidence of febrile illness in the 6 months prior to initial parental concern was significantly higher in the children with autistic regression compared with those with ASD (30% vs 0%; p<0.001). The overall prevalence of familial autoimmunity was also higher in children with autistic regression compared with those with ASD (33% vs 12%; p<0.001). Type 1 diabetes and autoimmune thyroiditis were both more common in families with children with autistic regression. Other non-immune risk factors did not differ between the two groups. INTERPRETATION: Our findings suggest that predisposition to autoimmunity, and immune/inflammatory activation, may be associated with autistic regression.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".