Differentiating Autism Spectrum Disorder From Other Developmental Delays In The First Two Years Of Life
Bibliographic record
Abstract
Advances in the identification of the early signs of autism spectrum disorder (ASD) have occurred despite the heterogeneity of the disorder and its variable onset and presentation. Using various methodologies including retrospective studies, community samples, and sibling cohorts, researchers have identified behavioral markers of the disorder that emerge over the first 2 years of life. However, there are characteristics of ASD that overlap with other types of developmental delay (DD), which may complicate differential diagnosis in young children. A review of the literature was conducted to identify the most promising behavioral markers that distinguish ASD from other types of DD in the first 2 years of life. The review identified profiles of behavioral markers in the social realm by 12 months and in the communication realm by 18 months, which along with additional atypical motor behaviors could distinguish ASD from DD. This constellation of features coupled with a flat or declining trajectory in specific aspects of social and communication development, may assist clinicians in targeting early interventions to at-risk infants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".