Autism traits in transgender and gender-diverse adults seeking gender-affirming medical treatment
Bibliographic record
Abstract
Background Prior research has indicated a common overlap between autism traits and gender identity diversity, but studies including nonbinary/genderqueer (NBGQ) adults in clinical settings are lacking. This study therefore compared autism traits in transgender and gender-diverse (TGD) adults seeking gender-affirming medical treatment (GAMT) with autism traits in cisgender adults. It further assessed differences in autism traits among TGD subgroups, considering sex assigned at birth (comparing adults assigned male at birth (AMAB) and female at birth (AFAB)) and gender identity (comparing NBGQ adults with binary transgender and cisgender adults).Aim The primary aim of the study was to investigate autism traits in TGD adults seeking GAMT, contrasting them with cisgender adults from the general population. Additionally, the study aimed to assess potential differences in autism traits within and between TGD subgroups, considering sex assigned at birth and gender identity.Methods The study sample included 424 TGD adults referred to a gender identity clinic in the Netherlands (Mdn age = 22.59, 54.2% AFAB) and an age-matched sample of 424 cisgender adults from the Netherlands and Flanders. Autism traits were assessed using the Social Responsiveness Scale for Adults (SRS-A).Results TGD adults reported significantly more autism traits on the SRS-A compared to cisgender adults. TGD adults AFAB displayed significantly more autism traits than both TGD adults AMAB and cisgender men and women. However, TGD AMAB and cisgender men did not differ significantly in autism traits. In the TGD group, 65 (15.3%) identified with a NBGQ identity, and this TGD subgroup reported significantly more autism traits compared to binary identifying TGD and cisgender adults.Conclusion & discussion The study confirmed a higher prevalence of autism traits in TGD adults, specifically in those AFAB and those identifying as NBGQ, emphasizing the need for personalized clinical approaches in GAMT for autistic individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".