Indicators of Simulated Driving Skills in Adolescents with Autism Spectrum Disorder
Bibliographic record
Abstract
Adolescents are at high risk for motor vehicle crashes (MVCs). Teens with autism spectrum disorder (ASD) may have an even greater risk for MVCs due to impaired visual, cognitive, and motor skills critical for driving. This prospective two group study demonstrated the demographic, clinical, and simulated driving skill differences of seven adolescents with ASD (mean age = 15.14, SD ±1.22) compared to 22 healthy controls (HC) (mean age = 14.32, SD ±.72) through a comprehensive driving evaluation (CDE) conducted by an occupational therapist certified driving rehabilitation specialist (OT-CDRS). Adolescents with ASD performed poorer on right eye acuity (Fischer’s (F) = 13.44, p = .003), cognition (Mann-Whitney Statistic (U) = 29.00, p = .01), visual motor integration (U = 27.50, p = .01), motor coordination (U = 5.00, p = .001), operational skills for managing simulator controls (U = 4.00, pU = 30.50, p = .02), speed regulation (U = 13.50, p = .001), lane maintenance (U = 34.00, p = .03), signaling (U = 38.50, p = .03), and adjustment to stimuli (U = 9.00, pU = 5.00, pConclusion). Compared to the HC, adolescents with ASD performed worse on visual, cognitive, motor, simulator operational, and fitness to drive skills, suggesting that an OT-CDRS may play an important role in assessing teens with ASD before they pursue traditional driver’s education.
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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 | 0.001 |
| 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".