Gender variance in children and adolescents with autism spectrum disorder from the National Database for Autism Research
Bibliographic record
Abstract
Previous studies suggest elevated rates of gender variance (GV), the wish to be of the other gender, in those with autism spectrum disorder (ASD). This study aimed to understand the rate of GV in children and adolescents with ASD and explore differences in sex, age, and emotional-behavioral problems relative to those referred to clinical services for mental health concerns (“referred”) and to the general population (“non-referred”). A secondary analysis of data from the National Database for Autism Research was used to explore GV using a child behavior checklist, parent report, in 176 children aged 6 to 18 year with ASD compared to referred and non-referred cohorts. GV was present in 4.0% of the ASD group, higher than for the non-referred group (0.7%) but similar to the referred group (4.0%). There were no significant sex differences in GV prevalence (males 3.7%, females 6.0%) in the ASD group. That the GV rate was elevated in ASD relative to non-referred samples but similar to clinically referred samples suggests that elevated rates of GV were not specific to ASD and may be more broadly associated with neurodevelopmental and psychiatric disorders of childhood. Further population-based research using clinical assessment for gender dysphoria is required in individuals with ASD and other neurodevelopmental disorders.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".