Early social communicative skills as predictors of symptom severity in autism spectrum disorder
Bibliographic record
Abstract
Background and aims Early diagnosis of autism spectrum disorder, while providing many benefits, also presents challenges. Accurately predicting symptom severity allows clinicians to confidently diagnose and assign the most appropriate intervention. Little available research predicts symptom severity in children with autism spectrum disorder who have not been exposed to significant levels of intervention. Methods The present file review study examined preverbal skills as predictors of symptom severity, approximately one year later, in a group of young children (18–64 months) with autism spectrum disorder ( n = 199). Results Of the two core diagnostic features (social communicative deficits and restricted repetitive behaviors), social communicative skills best predicted symptom severity. Furthermore, social communicative gestures predicted symptom severity after age, adaptive behavior, restricted repetitive behaviors, and functional gestures had been accounted for. Conclusions Social communicative gestures are excellent predictors of future symptom severity independent of intervention effects in very young children with autism spectrum disorder. Implications Previously, the social aspect of gestures has been missing in the literature on language and symptom prediction in children with autism spectrum disorder. Careful attention to social communicative gestures in the future may help with early diagnosis and more accurate predictions of symptom and developmental trajectories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".