Childhood Facial Recognition Predicts Adolescent Symptom Severity in Autism Spectrum Disorder
Bibliographic record
Abstract
Limited accuracy and speed in facial recognition (FR) and in the identification of facial emotions (IFE) have been shown in autism spectrum disorders (ASD). This study aimed at evaluating the predictive value of atypicalities in FR and IFE for future symptom severity in children with ASD. Therefore we performed a seven-year follow-up study in 87 children with ASD. FR and IFE were assessed in childhood (T1: age 6-12) using the Amsterdam Neuropsychological Tasks (ANT). Symptom severity was assessed using the Autism Diagnostic Observation Schedule (ADOS) in childhood and again seven years later during adolescence (T2: age 12-19). Multiple regression analyses were performed to investigate whether FR and IFE in childhood predicted ASD symptom severity in adolescence, while controlling for ASD symptom severity in childhood. We found that more accurate FR significantly predicted lower adolescent ASD symptom severity scores (ΔR(2) = .09), even when controlling for childhood ASD symptom severity. IFE was not a significant predictor of ASD symptom severity in adolescence. From these results it can be concluded, that in children with ASD the accuracy of FR in childhood is a relevant predictor of ASD symptom severity in adolescence. Test results on FR in children with ASD may have prognostic value regarding later symptom severity.
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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 | 0.004 |
| 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 | 0.000 |
| 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".