Characteristics of Autism Spectrum Disorder in Children with Gender Dysphoria
Bibliographic record
Abstract
Evidence suggests a higher than expected co-occurrence of gender dysphoria (GD) and autism spectrum disorder (ASD). However, the specificity of this link in GD (vs. other clinically referred) children remains unclear. The present study addressed this gap by comparing clinically referred children with GD (n = 61; 45 birth-assigned boys, 16 birth-assigned girls; M = 7.97 years) to a clinical control (CC) group (n = 40; 28 birth-assigned boys, 12 birth-assigned girls; M = 9.48 years), using two ASD-specific parent-report measures: the Social Responsiveness Scale (SRS) and the Social Communication Questionnaire (SCQ). In the GD group, 21.3% (n = 13; 12 birth-assigned boys) had a clinical ASD diagnosis; none of the children in the CC group had an ASD diagnosis. No significant group differences were found for ASD traits as measured by the SRS, with about 50% of each group receiving clinical-range scores. However, significant group differences emerged on the SCQ, with the GD group having significantly higher scores than the CC group. The SRS Autistic Mannerisms subdomain and the SCQ RRB subdomain did not show significant group differences, but an exploratory analysis, collapsed across sex, revealed significantly more parent-reported RRB symptoms for the GD than the CC group. One SCQ RRB item pertaining to unusual interests was endorsed significantly more often for the GD group (37.2%) than the CC group (7.7%). Taken together, the current study found that a greater percentage of children with GD had an ASD diagnosis compared to the CC group, but evidence for specificity on the two dimensional measures was mixed. Theoretical and clinical considerations are discussed.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".