Review of Clinical Studies Targeting Inflammatory Pathways for Individuals With Autism
Bibliographic record
Abstract
Immune dysfunction and abnormal immune response may be associated with certain mechanisms underlying autism spectrum disorder (ASD). The early evidence for this link was based on the increased incidence of ASD in children with a history of maternal infection during pregnancy. Observational studies show increased prevalence of immune-related disorders-ranging from atopy, food allergy, viral infections, asthma, primary immunodeficiency, to autoimmune disorders-in individuals with ASD and their families. Evidence of neuroglial activation and focal brain inflammation in individuals with ASD implies that the central nervous system immunity may also be atypical in some individuals with ASD. Also, both peripheral and central inflammatory responses are suggested to be associated with ASD-related behavioral symptoms. Atypical immune responses may be evident in specific ASD subgroups, such as those with significant gastrointestinal symptoms. The present review aimed to evaluate current literature of potential interventions that target inflammatory pathways for individuals with ASD and to summarize whether these interventions were associated with improvement in autism symptoms and adaptation. We found that the current literature on the efficacy of anti-inflammatory interventions in ASD is still limited and large-scale randomized controlled trials are needed to provide robust evidence. We concluded that the role of immune-mediated mechanisms in the emergence of ASD or related challenges may be specific to subsets of individuals (e.g. those with concurrent immunological disorders, developmental regression, or high irritability). These subsets of individuals of ASD might be more likely to benefit from interventions that target immune-mediated mechanisms and with whom next-stage immune-mediated clinical trials could be conducted.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".