Factors affecting the age at diagnosis of autism spectrum disorders in Nova Scotia, Canada
Bibliographic record
Abstract
While early diagnosis of autism spectrum disorders (ASD) is essential for ensuring timely access to early intervention services, there is limited existing literature investigating factors that delay this diagnosis. This population-based cohort study explored the age at which children in Nova Scotia, Canada, are diagnosed with ASDs and the factors associated with this age. Children diagnosed with an ASD between January 1992 and December 2005 were identified from a cohort of live births in the province between 1990 and 2002. Demographic and clinical variables were extracted from population-based perinatal and administrative health databases and evaluated as predictors of age at ASD diagnosis. Of 122,759 live births, 884 cases of ASDs were identified during the study period. The median age at diagnosis within the cohort was 4.6 years. In adjusted linear regression analysis, a one year increase in maternal age at delivery was associated with a 0.06 decrease in age at ASD diagnosis (p= .0007). Children who were residents of Halifax County received their diagnoses 0.52 years later than residents of other counties (p= .0054). A diagnosis of attention-deficit/hyperactivity disorder (ADHD) was associated with a 1.29-year increase in age at diagnosis (p< .0001). These results suggest that potential exists for improving early detection of ASDs in the province. Future research in this field has the potential to contribute to our understanding of the causal pathways linking the demographic and clinical variables we have identified and the age at diagnosis of ASDs.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".