Sex‐Differential Trajectories of Domain‐Specific Associations Between Autistic Traits and Co‐Occurring Emotional‐Behavioral Concerns in Autistic Children
Bibliographic record
Abstract
Assessing autistic traits alongside co-occurring emotional/behavioral concerns (EBCs) is challenging due to their overlap in clinical presentations, which can vary by age and sex. This study aimed to investigate domain-specific associations between autistic traits and EBCs-including anxiety, affective, attention-deficit/hyperactivity, and oppositional-defiant problems-across childhood in autistic boys and girls. We prospectively followed 389 children (84% male) diagnosed with autism at ages 2-5 years, using the Social Responsiveness Scale (SRS) and Child Behavior Checklist (CBCL) across eight timepoints until age 12. Moderated nonlinear factor analysis was used to identify and adjust for measurement non-invariance of SRS items by age, sex, and EBCs. The adjusted scores were then used for sex-moderated time-varying modeling of associations between autistic traits and EBCs. Several SRS items in the domains of social-interaction difficulties and repetitive mannerisms showed significant intercept bias by age and level of co-occurring anxiety and ADHD (effect size r > 0.20). In autistic boys, strong associations were observed between social-communication difficulties and EBCs around ages 7-9, which tended to diminish in late childhood. In contrast, autistic girls showed stable or intensifying associations, particularly with anxiety, into late childhood. Results revealed significant associations between autistic traits and EBCs after addressing item-level measurement biases. The varying associations over time highlight the importance of continuous monitoring to promptly address autistic children's sex-differential mental health needs. These findings emphasize the benefits of refining behavioral constructs and adopting a nuanced developmental approach to identify critical periods of symptom coupling/decoupling for informing evaluation and service provision.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".