Bibliographic record
Abstract
Autism (AUT) is one of the most prevalent developmental disorders emerging during childhood, and can be amongst the most incapacitating mental disorders. Some individuals with AUT require a lifetime of supervised care. Autism Speaks reported estimated costs for 2012 at £34 billion in the UK; and $3.2 million-$126 billion in the US, Australia and Canada. Ethnicity and migration experiences appear to increase risks of AUT and relate to underlying biological risk factors. Sociobiological stress factors can affect the uterine environment, or relate to stress-induced epigenetic changes during pregnancy and delivery. Epigenetic risk factors associated with AUT also include poor pregnancy conditions, low birth weight, and congenital malformation. Recent studies report that children from migrant communities are at higher risk of AUT than children born to non-migrant mothers, with the exception of Hispanic children. This paper provides the first systematic review into prevalence and predictors of AUT with a particular focus on maternal migration stressors and epigenetic risk factors. AUT rates appear higher in certain migrant communities, potentially relating to epigenetic changes after stressful experiences. Although AUT remains a rare disorder, failures to recognize its public health urgency and local community needs continue to leave certain cultural groups at a disadvantage.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| 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) | 0.005 | 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".