Driving Characteristics of Teens With Attention Deficit Hyperactivity and Autism Spectrum Disorder
Bibliographic record
Abstract
Vehicle crashes are a leading cause of death among teens. Teens with attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), or both (ADHD-ASD) may have a greater crash risk. We examined the between-groups demographic, clinical, and predriving performance differences of 22 teens with ADHD-ASD (mean age = 15.05, standard deviation [SD] = 0.95) and 22 healthy control (HC) teens (mean age = 14.32, SD = 0.72). Compared with HC teens, the teens with ADHD-ASD performed more poorly on right-eye visual acuity, selective attention, visual-motor integration, cognition, and motor performance and made more errors on the driving simulator pertaining to visual scanning, speed regulation, lane maintenance, adjustment to stimuli, and total number of driving errors. Teens with ADHD-ASD, compared with HC teens, may have more predriving deficits and as such require the skills of a certified driving rehabilitation specialist to assess readiness to drive.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".