Towards equitable diagnoses for autism and attention-deficit/hyperactivity disorder across sexes and genders
Bibliographic record
Abstract
PURPOSE OF REVIEW: Sex/gender-related factors contribute to contextual issues influencing the recognition of autism and attention-deficit/hyperactivity disorder (ADHD), and modulate how neurodevelopmental characteristics are manifested. This review summarizes the empirical literature to provide directions for improving clinical diagnostic practices. RECENT FINDINGS: Timing of autism and/or ADHD diagnosis, particularly in girls/women, is related to the individual's developmental characteristics and co-occurring diagnoses, and expectancy, alongside gender stereotype biases, of referral sources and clinicians. This is further compounded by sex and gender modulations of behavioural presentations. The emerging 'female autism phenotype' concept may serve as a helpful illustration of nuanced autism phenotypes, but should not be viewed as essential features of autism in a particular sex or gender. These nuanced phenotypes that can present across sexes and genders include heightened attention to socially salient stimuli, friendship and social groups, richness in language expression, and more reciprocal behaviours. The nuanced female-predominant ADHD phenotypes are characterized by subtle expressions in hyperactivity-impulsivity (e.g., hyper-verbal behaviours). Optimizing neurodevelopmental diagnoses across sexes and genders also requires an understanding of sex-related and gender-related variations in developmental trajectories, including compensation/masking efforts, and the influences of co-occurring conditions on clinical presentations. SUMMARY: Equitable diagnoses across sexes and genders for autism and ADHD require understanding of the nuanced presentations and the Gestalt clinical-developmental profiles, and addressing contextual biases that influence diagnostic practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".