Sex Differences in Age of Diagnosis and First Concern among Children with Autism Spectrum Disorder
Bibliographic record
Abstract
OBJECTIVE: Early identification of autism spectrum disorder (ASD) is an essential healthcare priority. Girls may be at risk for late diagnosis, although research is equivocal regarding how sex and other factors relate to ASD identification. The goals of the current investigation were to (1) identify how child sex, cognitive abilities, and demographic factors relate to age of first concern (AOC) and age of diagnosis (AOD), (2) evaluate trends in AOC/AOD over time, and (3) consider whether main effects of sex on AOC/AOD are moderated by cognitive abilities or time. METHOD: Children (N = 365; 20% female; 85.6% identified as White) with ASD participated through the Province of Ontario Neurodevelopmental Disorders (POND) Network. Study records included AOD, date/timing of diagnosis (between 1996 and 2017), age of first parent concern, demographics, and standardized cognitive testing results (24.7% of children had IQ scores below standard scores of 70). RESULTS: Average AOC occurred before 2 years of age whereas average AOD occurred after 5 years of age. Girls did not differ on AOC but had a later AOD than boys. Higher verbal IQ was associated with later AOD more strongly in girls than boys. Regarding time-related changes, average AOC and AOD increased across the study period, more strongly for girls. CONCLUSIONS: Results support that sex is a key factor underlying delays in ASD identification and highlight the urgent need to improve diagnostic practices among girls. Limitations and implications for improving the diagnostic process are discussed. ASD=autism spectrum disorder; IQ=intelligence quotient; AOC=parental report of age of first concern; AOD=age of diagnosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".