Sex differences in the prevalence of common comorbidities in autism: a narrative review
Bibliographic record
Abstract
Autism spectrum disorder involves challenges in social communication and restricted, repetitive behaviors. Historically, males have received autism diagnoses at comparatively high rates, prompting an underrepresentation of females in research and an incomplete understanding of sex-specific symptom presentations and comorbidities. This review examines sex differences in the prevalence of common comorbidities of autism to inform tailored clinical practices. These conditions include attention deficit hyperactivity disorder, anxiety disorders, conduct disorder, depression, epilepsy, intellectual disability, and tic disorders. Attention deficit hyperactivity disorder is prevalent in both sexes; however, females may more frequently exhibit the inattentive subtype. Anxiety disorders display inconsistent sex differences, while conduct disorder more frequently impacts males. Depression becomes more common with age; some studies indicate more pronounced symptoms in adolescent girls, while others suggest greater severity in males. Epilepsy is more prevalent in females, especially those with intellectual disabilities. Despite displaying a male predominance, intellectual disability may exacerbate the severity of autism to a greater degree in females. No clear sex differences have been found regarding tic disorders. Overall, contributors to sex-based differences include biases stemming from male-centric diagnostic tools, compensatory behaviors like camouflaging in females, genetic and neurobiological differences, and the developmental trajectories of comorbidities. Recognizing these factors is crucial for developing sensitive diagnostics and sex-specific interventions. Inconsistencies in the literature highlight the need for longitudinal studies with large, diverse samples to investigate autism comorbidities across the lifespan. Understanding sex differences could facilitate earlier identification, improved care, and personalized interventions, thus enhancing quality of life for individuals with autism.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".