General paediatric practice in autism spectrum disorder screening in Ontario, Canada: Opportunities for improvement
Bibliographic record
Abstract
BACKGROUND: Screening is important for early identification of children with autism spectrum disorder (ASD), potentially leading to earlier intervention. Research has identified some barriers to early identification of ASD, however, information about ASD screening in Canadian general paediatric practice is lacking. OBJECTIVES: The aim of the study is to better understand ASD screening practice patterns by examining the use of ASD and general developmental screening tools by general paediatricians. METHODS: The research team conducted a cross-sectional survey of general paediatricians. RESULTS: Two-hundred and sixty-seven paediatricians responded and 132 were eligible for the study. Ninety-three per cent of the responders used a developmental screening tool. Eighty-five per cent of the responders used an ASD screening tool when there were concerns for ASD, and 15% never used one. The most commonly used ASD screening tool was the M-CHAT. Children suspected of having ASD were referred to specialists not only to confirm the diagnosis but also to facilitate access to resources. General paediatricians were keen to incorporate formal ASD screening tools in their practice but identified the need for clearer guidelines. CONCLUSION: Previous studies have shown that children at risk of ASD continue to be missed through developmental surveillance and targeted screening. Paediatricians are interested in implementing an ASD screening tool and cite brevity and forms that can be completed by parents as factors that would support the use of a screening tool. Clearer guidelines and tools to support ASD screening and access to resources are needed.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".