Comparison of the SIMARD MD to Clinical Impression in Assessing Fitness to Drive in Patients with Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: The assessment of fitness to drive in patients with cognitive impairment is complex. The SIMARD MD was developed to assist with assessing fitness to drive. This study compares the clinical decision made by a geriatrician regarding driving with the SIMARD MD score. METHODS: Patients with a diagnosis of mild dementia or mild cognitive impairment, who had a SIMARD MD test, were included in the sample. A retrospective chart review was completed to gather diagnosis, driving status, and cognitive and functional information. RESULTS: Sixty-three patients were identified and 57 met the inclusion criteria. The mean age was 77.1 years (SD 8.9). The most common diagnosis was Alzheimer's disease in 22 (38.6%) patients. The mean MMSE score was 24.9 (SD 3.34) and the mean MoCA was 19.9 (SD 3.58). The mean SIMARD MD score was 37.2 (SD 19.54). Twenty-four patients had a SIMARD MD score ≤ 30, twenty-eight between 31-70, and five scored > 70. The SIMARD MD scores did not differ significantly compared to the clinical decision (ANOVA p value = 0.14). CONCLUSIONS: There was no association between the SIMARD MD scores and the geriatricians' clinical decision regarding fitness to drive in persons with mild dementia or mild cognitive impairment.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".