Precursors of self‐regulation in infants at elevated likelihood for autism spectrum disorder
Bibliographic record
Abstract
Research concerning temperament in children and adults with autism spectrum disorder (ASD) has suggested a consistent profile of low positive affect, high negative affect, and low regulation (Visser et al., 2016). One area receiving less attention is individual differences among children diagnosed with ASD. The primary objective of this study was to use a person-centered approach to explore heterogeneity of early temperament precursors of regulation in a large sample of infants with elevated familial likelihood of ASD. Early precursors of regulation included temperament assessed at 6, 12, and 24 months whereas outcome measures were diagnosis of ASD, cognitive ability and adaptive behavior at 36 months. Participants included 176 low-likelihood and 473 elevated-likelihood infants, 129 of whom were diagnosed with ASD at 3 years. Results supported a three-profile solution: a well-regulated profile (high positive affect and high attentional focus and shifting), a low attention focus profile (higher attentional shifting compared to attentional focus), and a low attention shifting profile (higher attentional focus compared to attentional shifting). A higher proportion of children diagnosed with ASD were classified into the low attention shifting profile. Furthermore, children with the well-regulated profile were differentiated from the other profiles by a pattern of higher social competence and lower dysregulation whereas children with the low attention focus profile were distinguished from the other profiles by higher cognitive ability at 3 years. The findings indicate that the combination of early positive affect with attention measures may provide an enhanced tool for prediction of self-regulation and later outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".