Prevalence of autism according to maternal immigrant status and ethnic origin
Bibliographic record
Abstract
OBJECTIVE: To examine the rates of autism separately according to maternal immigrant status and ethnic origin in respect to the vitamin D insufficiency hypothesis. METHOD: Articles were identified by electronic searches. Studies were selected when they analysed autism rates according to maternal immigrant status and/or ethnic origin using multivariate techniques. RESULTS: This review gave further support to the association between maternal immigrant status and an increased risk of autism. The relationship with ethnic origin was more complex. Although the crude rates did not differ, multivariate analyses taking into account confounding factors found that black ethnicity was associated with an increased risk for autism. The risk was highly significant when considering the strict definition of autistic disorders as opposed to the large definition of other pervasive developmental disorders. The risk was also very significant for autism associated with mental retardation. CONCLUSION: These results are consistent with the maternal vitamin D insufficiency hypothesis. Neurobiological studies are warranted to document the effect of maternal vitamin D insufficiency during pregnancy on the foetal brain and the window of vulnerability. This review stresses the importance of monitoring vitamin D levels in pregnant women, especially those who are immigrant, dark-skinned or veiled, and the urgency of randomized controlled trials.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".