Driving indicators in teens with attention deficit hyperactivity and/or autism spectrum disorder
Bibliographic record
Abstract
BACKGROUND: Motor vehicle crashes are leading causes of death among teens. Those teens with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), or a dual diagnosis of ADHD/ASD have defining characteristics placing them at a greater risk for crashes. PURPOSE: This study examined the between-group demographic, clinical, and simulated driving differences in teens, representing three diagnostic groups, compared to healthy controls (HCs). METHOD: In this prospective observational study, we used a convenience sample of teens recruited from a variety of community settings. FINDINGS: Compared to the 22 HCs (mean age = 14.32, SD = +/-.72), teen drivers representing the diagnostic groups (ADHD/ASD, n = 6, mean age = 15.00, SD = +/-.63; ADHD, n = 9, mean age = 15.00, SD = +/- 1.00; ASD, n = 7, mean age = 15.14, SD = +/-. 1.22) performed poorer on visual function, visual-motor integration, cognition, and motor performance and made more errors on the driving simulator. IMPLICATIONS: Teens from diagnostic groups have more deficits driving on a driving simulator and may require a comprehensive driving evaluation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".