Risk of Autism Spectrum Disorders in Children Born to Mothers With Rheumatoid Arthritis: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: There is recent evidence to suggest that in utero exposure to maternal antibodies and cytokines is an important risk factor for autism spectrum disorders (ASDs). We aimed to systematically review the risk of ASDs in children born to mothers with rheumatoid arthritis (RA) compared to children born to mothers without RA. METHODS: We conducted a systematic review of original articles using the electronic databases PubMed, Embase, and Web of Science. RESULTS: Our literature search yielded a total of 70 articles. Of the potentially relevant studies retrieved, 67 were excluded for lack of relevance and/or because they did not report original data. Three studies were included in the final analysis. A case-control study found no difference in the prevalence of RA in mothers of children with ASDs versus control mothers. Another case-control study showed a statistically significant 8-fold increase in autoimmune disorders, including RA, in mothers of offspring with ASDs compared to controls. Forty-six percent of offspring with ASDs had a first-degree relative with RA, compared to 26% of controls. And in a population-based cohort study, investigators observed an increased risk of ASDs in children with a maternal history of RA compared to children born to unaffected mothers. These studies had methodologic limitations: none controlled for medication exposures, only 1 controlled for obstetric complications and considered the timing of RA diagnosis in relation to pregnancy, and all but 1 used a case-control study design. CONCLUSION: Observational studies suggest a potentially increased risk of ASDs in children born to mothers with RA compared to children born to mothers without RA, although data are limited.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".