The SIMARD-MD is not an Effective Driver Screening Tool for Determining Fitness-To-Drive
Bibliographic record
Abstract
BACKGROUND: Studies have reported poor sensitivity and specificity of the Screen for the Identification of Cognitively Impaired Medically At-Risk Drivers, a modification of the DemTech (SIMARD-MD) to screen for drivers with cognitive impairment. The purpose of this study was to determine whether the SIMARD-MD can accurately predict pass/fail on a road test in drivers with cognitive impairment (CI) and healthy drivers. METHODS: Data from drivers with CI were collected from two comprehensive driving assessment centres (n=86) and compared with healthy drivers (n=30). All participants completed demographic measures, clinical measures, and a road rest (pass/fail). Analyses consisted of correlations between the SIMARD-MD and the other clinical measures, and a receiver-operating-characteristic (ROC) curve to determine the predictive ability of the SIMARD-MD. RESULTS: < .001). The ROC curve showed the SIMARD-MD, regardless of any cut-point, misclassified a large number of CI individuals (AUC=.692; 95% CI = 0.578, 0.806). CONCLUSIONS: Given the high level of misclassification, the SIMARD-MD should not be used with either healthy drivers or those with cognitive impairment for making decisions about driving.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 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".