Driving performance and its correlation with cognitive tests in elderly drivers with cognitive impairments
Bibliographic record
Abstract
Abstract Background With the rapid aging of the population, the driving problem of dementia patients has caused increasing concerns among the government, researchers and the public worldwide1. We aim to investigate driving behaviors of elderly drivers with cognitive impairments using a driving simulator. And we are further interested in exploring the correlation between neuropsychological tests and driving performance. Methods Elderly subjects with driving experience who visited the outpatient neurology clinic in the Kyoto university hospital for cognitive complaints from July 2018 to December 2019 were investigated. Cognitive tests were carried out, as well as a questionnaire asking both demographic and driving characteristics. Participants who wished to continue driving were asked to complete the driving tests. Results A total of 45 participants were included in the study, ranged in age from 66 to 92 years, of which 57.8% were male. Slightly more than half of participants have returned their driving licenses whereas 20 still hold a valid driving license. Though current drivers had better cognitive functions than retired drivers, poor performance was found on reaction, starting and stopping, signaling, and general. The score of Mini‐mental state examination (MMSE), trail making test (TMT), block design test (BDT) and Alzheimer’s disease assessment scale–cognitive subscale (ADAS‐Cog) were correlated with reaction tests, especially TMT‐B, revealing a slightly higher correlation coefficients for the choice reaction test. MMSE, BDT and ADAS‐Cog had quite high correlation coefficients for sudden braking (ρ1=‐0.806, ρ2=‐0.727, ρ3=0.786, p<0.001). TMT‐B was associated with no signaling (ρ4=0.625, p<0.05) while clock drawing test (CDT) had a correlation with wrong signaling (ρ5=‐0.545, p<0.05). In addition, CDT also showed a negative correlation with going the wrong way (ρ6=‐0.655, p<0.05). MMSE and ADAS‐Cog were moderately correlated with lateral control (ρ7=‐0.620, ρ8=0.560, p<0.05). Conclusions Current elderly drivers had better cognitive functions, but a considerable part of them showed poor performance in the driving tests. Single cognitive tests were shown to have moderate to high correlations with specific driving maneuvers segmented from driving performance. Reference: Kim YJ, An H, Kim B, Park YS, Kim KW. An International Comparative Study on Driving Regulations on People with Dementia. J Alzheimers Dis. 2017;56(3):1007‐1014.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".