Comparative Analysis of Phenotypic and Genotypic Differences Between Individuals Affected by Regressive and Non‐Regressive Autism: A Cross‐Sectional Study
Bibliographic record
Abstract
Development among autistic youth varies widely. A subgroup of children experiences regression, defined as the loss of previously acquired developmental skills. Various genetic and environmental factors have been suggested as potential contributors. This study aimed to compare the developmental profiles of children and youth with regression to those without and identify factors associated with regression. Data from the Province of Ontario Neurodevelopmental Disorders (POND) Network was analyzed, including 930 eligible participants. Regression classification was based on the Autism Diagnostic Interview-Revised (ADI-R). Differences in demographic information, medical history, mental health, cognitive and adaptive functioning, and molecular genetic findings were examined between individuals with regressive and non-regressive autism. Among participants, 211 (22.7%) had regressive autism. Lower Full-Scale IQ (p corrected = 0.015) and adaptive function (ABAS-2) scores (p corrected = 0.015) were identified in the regressive group. No statistically significant differences in mental health outcomes (measured by the Child Behavior Checklist, CBCL) or socialization and core symptom severity (measured by the Social Communication Questionnaire, SCQ) were found. There were no notable differences in other factors hypothesized to contribute to regression, such as pregnancy duration, family history of autism, caregivers' education levels, or sleep disorders, except for a higher prevalence of epilepsy in the regressive group (p = 0.001). Rare and common genetic features of both groups are described. In conclusion, autistic youth with regression tend to have lower cognitive and adaptive scores and may experience higher epilepsy rates. Further powered studies are needed to explore the genomic architecture of autistic regression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".