Editorial: Secondary vs. Idiopathic Autism
Bibliographic record
Abstract
Secondary vs. Idiopathic AutismThis Research Topic consists of four articles, contributed by 30 authors, each focusing on various aspects of secondary autism spectrum disorder (ASD).The articles in this Research Topic discuss some of the controversies and progress made in how to assess a child with secondary ASD.They discuss the utility of using genetic and metabolic tests, as well as combinations of the same.They also grapple with the relationship of intellectual disability (ID) and other symptoms in patients with secondary ASD.The diagnostic boundaries of the behavioral phenotype that define ASD are fairly broad due to the large variability that is observed in symptom types, onset, and severity.This variability serves as an index of etiological heterogeneity for a group of complex conditions where multiple genetic/ environmental hits provide an additive burden to symptom expression.Within this spectrum of disorders our Research Topic examines a subpopulation of patients where the ASD phenotype occurs and a specific cause can be identified (1).These cases are often referred to as "secondary" ASD as a way of distinguishing them from "idiopathic" ASD.While there is some variation of the estimates, about 85% of individuals with ASD are termed as having idiopathic ASD with about 15% diagnosed with secondary ASD (2).Cases of secondary ASD display significant etiological diversity and, sometimes, more severe behavioral problems than individuals having idiopathic ASD (3, 4).Well documented causes of secondary ASD include conditions such as tuberous sclerosis, down syndrome, Fragile X syndrome (FXS), and some congenital infections (e.g., cytomegalovirus) (5-7).Differentiating between idiopathic and secondary ASD is essential when drawing conclusions regarding pharmacological and behavioral treatments among published studies (8).Clinical study design benefits from the selection of homogeneous populations, those without prognostically distinct subgroups, as it makes it easier to assess treatment efficacy (vide infra, (9)].Indeed, a thorough understanding of the different clinical presentations, natural history and even life expectancies of persons with ASD should go beyond the influence of traditional variables and use relevant classifiers of biomedical and sociocultural differences that may have an impact on the health and wellbeing of the studied subject population.Genomic scanning is beginning to elucidate the underlying genetic architecture that confers ASD susceptibility.Chromosomal and penetrant gene abnormalities are common, contributing to about 5%-15% of ASD cases in some estimates (10).By way of contrast, de novo chromosomal copy number variants, important contributors to lower IQ, and diminished motor skills (11), are seen in
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.020 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".