Genetic and Neurodevelopmental Influences in Autistic Disorder
Bibliographic record
Abstract
OBJECTIVE: In the past, autism was considered to be largely psychogenic. However, research in the last 2 decades indicates that autism is largely caused by genetic factors that lead to abnormal brain development. This article reviews research into the genetic and neurodevelopmental factors underlying autism. METHODS: We review the findings from genetic and brain-imaging studies of autism over the past 15 years and synthesize these findings as a guide for future research. RESULTS: Genome scans and association studies have suggested potential genomic regions and genes, respectively, that may be involved in the etiology of autism, and there have been some replications of these results. Similarly, the findings that brain volume is exaggerated in autism and corpus callosum size is reduced have also been independently replicated. Unfortunately, studies of other subcortical structures remain inconclusive or contradictory. CONCLUSIONS: Overwhelming evidence now supports a neurobiological basis for autism. However, further refinements will be needed to guide future studies, particularly to identify the most informative phenotypes to investigate. Additionally, studies examining the role of genetic factors in the brain abnormalities underlying autism will likely lead to further findings that will enhance our understanding of autism's causes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".