The Autism Parent Screen for Infants: Predicting risk of autism spectrum disorder based on parent-reported behavior observed at 6–24 months of age
Bibliographic record
Abstract
This study examined whether a novel parent-report questionnaire, the Autism Parent Screen for Infants, could differentiate infants subsequently diagnosed with autism spectrum disorder from a high-risk cohort (siblings of children diagnosed with autism spectrum disorder (n = 66)) from high-risk and low-risk comparison infants (no family history of autism spectrum disorder) who did not develop autism spectrum disorder (n = 138 and 79, respectively). Participants were assessed prospectively at 6, 9, 12, 15, 18, and 24 months of age. At 36 months, a blind independent diagnostic assessment for autism spectrum disorder was completed. Parent report on the Autism Parent Screen for Infants was examined in relation to diagnostic outcome and risk status (i.e. high-risk sibling with autism spectrum disorder, high-risk sibling without autism spectrum disorder, and low-risk control). The results indicated that from 6 months of age, total score on the Autism Parent Screen for Infants differentiated between the siblings with autism spectrum disorder and the other two groups. The sensitivity, specificity, and positive and negative predictive validity of the Autism Parent Screen for Infants highlight its potential for the early screening of autism spectrum disorder in high-risk cohorts.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".