Establishing the Predictive Validity of the ScanCourse for Assessing On-Road Driving Performance
Bibliographic record
Abstract
IMPORTANCE: Scanning the environment is critical for driving safety. The ScanCourse is a functional assessment that assesses a person's ability to scan the environment for visual information while in motion. Measurement properties for the ScanCourse have been reported; however, its predictive validity is unknown. OBJECTIVE: To determine the predictive validity of the ScanCourse for on-road driving performance and establish clinical cutoff scores. DESIGN: Retrospective chart reviews were conducted over a 6-mo period. SETTING: Four Canadian driver rehabilitation programs. PARTICIPANTS: Charts from patients with neurological or vision conditions were eligible if they contained ScanCourse and on-road driving evaluation results between September 1, 2008, and August 30, 2018. Three hundred twenty-five charts were included for analysis. OUTCOMES AND MEASURES: Area under the curve (AUC) analysis was used to determine the predictive validity of ScanCourse scores for on-road outcomes; cutoff scores were established by optimizing sensitivity and specificity. RESULTS: The ScanCourse had an AUC of .702. The optimal cutoff score was 18/20 with a sensitivity of 76.7% and a specificity of 47.1%. CONCLUSIONS AND RELEVANCE: Assessing the scanning abilities of at-risk drivers who intend to return to driving after sustaining an injury can help identify safety risks and inform interventions. The ScanCourse was found to have acceptable discriminatory ability for on-road driving performance. This study provides evidence supporting its continued use as a screening tool to assess driver fitness with an identified optimal cutoff score for clinical use. WHAT THIS ARTICLE ADDS: Measuring the predictive ability of the ScanCourse assessment in relation to on-road driving performance provides occupational therapists with an evidence-based clinical tool to assist with screening fitness to drive among at-risk people.
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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".