Sex differences in scores on standardized measures of autism symptoms: a multisite integrative data analysis
Bibliographic record
Abstract
BACKGROUND: Concerns have been raised that scores on standard measures of autism spectrum disorder (ASD) symptoms may differ as a function of sex. However, these findings are hindered by small female samples studied thus far. The current study evaluated if, after accounting for age, IQ, and language level, sex affects ASD severity estimates from diagnostic measures among children with ASD. METHODS: Data were obtained from eight sources comprising 27 sites. Linear mixed-effects models, including a random effect for site, were fit for 10 outcomes (Autism Diagnostic Observation Schedule [ADOS] domain-level calibrated severity scores, Autism Diagnostic Interview-Revised [ADI-R] raw scores by age-based algorithm, and raw scores from the two indices on the Social Responsiveness Scale [SRS]). Sex was added to the models after controlling for age, NVIQ, and an indicator for language level. RESULTS: Sex significantly improved model fit for half of the outcomes, but least square mean differences were generally negligible (effect sizes [ES] < 0.20), increasing to small to moderate in adolescence (ES < 0.40). Boys received more severe RRB scores than girls on both the ADOS and ADI-R (age 4 + algorithm), and girls received more severe scores than boys on both SRS indices, which emerged in adolescence. CONCLUSIONS: This study combined several available databases to create the largest sample of girls with ASD diagnoses. We found minimal differences due to sex beyond other known influences on ASD severity indicators. This may suggest that, among children who ultimately receive a clinical ASD diagnosis, severity estimates do not systematically differ to such an extent that sex-specific scoring procedures would be necessary. However, given the limitations inherent in clinically ascertained samples, future research must address questions about systematic sex differences among children or adults who do not receive clinical diagnoses of ASD. Moreover, while the current study helps resolve questions about widely used diagnostic instruments, we could not address sex differences in phenotypic aspects outside of these scores.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".