The SIMARD Screening Tool to Identify Unfit Drivers
Bibliographic record
Abstract
Dobbs and Schopflocher published an article in which they introduced a tool to identify people who are unfit to drive because of cognitive impairment. In our view, their conclusion that this tool has ". . . a high degree of accuracy that can be used for immediate decisions in the clinical setting"(1(p119)) is too strongly stated, particularly given that the cut-points they used yield false positive (FP) and false negative (FN) percentages in the 6% to 11% range. We believe the reason for using dual cut-points is to ensure that FP and FN fractions are both controlled very stringently, and that it would be more appropriate to set cut-offs that maintain both of them closer to 1%. Using our own data, we constructed two pairs of dual cut-points-one pair that yielded FP and FN percentages similar to those from the Dobbs and Schopflocher article and another pair that yielded FP and FN percentages no greater than 1%. For the first pair of cut-points, 53% of test results were indeterminate (compared to 50% for Dobbs and Schopflocher). For the second pair of cut-points, 86% of test results were indeterminate. Presumably, the same pattern would be observed in Dobbs and Schopflocher's data if their current dual cut-points were replaced with cut-points that controlled the FP and FN percentages at more appropriate levels. We also plotted receiver operating characteristic curves, and calculated the area under the curve (AUC) for the Screen for the Identification of Cognitively Impaired Medically At-Risk Drivers, A Modification of the DemTect (SIMARD-MD) and for the combination of the Mini-Mental State Examination and Trail-Making Test A (using our data for the latter). The difference between them was trivial (AUC = 0.75 and 0.72, respectively). Taken together, the results of the two analytic approaches suggest that other tools currently in use by physicians perform at least as well as the SIMARD-MD, and that it does not represent a significant breakthrough.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".