Early Identification of Autism Spectrum Disorder: Recommendations for Practice and Research
Bibliographic record
Abstract
Early identification of autism spectrum disorder (ASD) is essential to ensure that children can access specialized evidence-based interventions that can help to optimize long-term outcomes. Early identification also helps shorten the stressful "diagnostic odyssey" that many families experience before diagnosis. There have been important advances in research into the early development of ASDs, incorporating prospective designs and new technologies aimed at more precisely delineating the early emergence of ASD. Thus, an updated review of the state of the science of early identification of ASD was needed to inform best practice. These issues were the focus of a multidisciplinary panel of clinical practitioners and researchers who completed a literature review and reached consensus on current evidence addressing the question "What are the earliest signs and symptoms of ASD in children aged ≤24 months that can be used for early identification?" Summary statements address current knowledge on early signs of ASD, potential contributions and limitations of prospective research with high-risk infants, and priorities for promoting the incorporation of this knowledge into clinical practice and future research.
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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 | 0.109 | 0.275 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.013 | 0.009 |
| Research integrity | 0.020 | 0.015 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".