Autism spectrum disorder in infancy
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review explores recent literature to prioritize aspects of development to be targeted by intervention for infants and toddlers with autism spectrum disorder (ASD). RECENT FINDINGS: Recent investigation of early development in ASD, including prospective studies of infants at increased risk (i.e., those with an affected older sibling) identifies impairments in four key developmental domains that are predictive of ASD. These domains are early attentional control, emotion regulation, social orienting/approach, and communication development. Reciprocal relationships exist among these domains, both in ASD and in typical development. Thus, these domains represent key intervention targets, informing treatment models under investigation in recent clinical trials. SUMMARY: By targeting the earliest and foundational manifestations of atypical development, we can capitalize on neural plasticity and build skills that are most likely to have scaffolding effects on development. The optimal timing and procedures of intervention remain empirical questions, but as the field moves toward earlier identification of risk, we are now poised to evaluate the impact of tailored approaches before the developmental cascade that leads to ASD is fully manifested. Consideration regarding community translation of ASD-specific interventions for infants and toddlers is also needed, with a focus on feasibility, cost-effectiveness, and sustainability.
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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 | 0.003 | 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) | 0.007 | 0.002 |
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".