Validity of the Cognitive Behavioral Driver’s Inventory in Predicting Driving Outcome
Bibliographic record
Abstract
OBJECTIVE: This study seeks to (a) compare Cognitive Behavioral Driver's Inventory (CBDI) scores for clients who passed and failed a driving evaluation and for diagnostic groups (left cerebrovascular accident [CVA], right CVA, traumatic brain injury [TBI], and cognitive decline); (b) determine sensitivity, specificity, and positive and negative predictive values of the CBDI; (c) compare validity of the CBDI with other tools; and (d) identify factors associated with outcome. PARTICIPANTS: This historical cohort study included clients with neurological conditions who completed a driving evaluation. MEASURES: CBDI, Motor-Free Visual Perception Test (MVPT), Bells test, and driving results were extracted from the charts. RESULTS: Mean CBDI (p < 0.0001) and MVPT (p < 0.0001) scores were significantly worse for those failing compared to passing the driving evaluation. Sensitivity of the CBDI was 62%, specificity was 81%, positive predictive values were 73%, and negative predictive values were 71%. Results varied according to diagnostic group. CONCLUSIONS: The CBDI is not sufficiently predictive of outcome to replace a driving evaluation, and is predictive only for clients with R-CVA and TBI. Evaluation of driving should vary according to diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".