Repetitively Driven Trips as a Measure of Older Adult Driver Cognitive Health – Three Case Studies
Bibliographic record
Abstract
Older adult in-car driving data represents a valuable data source where successful measurement and interpretation could assist clinicians in driving fitness assessments. One of the measurement challenges with naturalistic driving assessments is the many possible sources of variability. Therefore, focusing measurements on a trip that is driven repetitively over a sustained period of time may reduce sources of variability and increase utility in driving assessments. In this study, repetitive-trips with two destinations that were driven at least 20 times during the first year of driving were investigated across a period of five years for three different older adult drivers with the goal of providing a preliminary evaluation of the value of repetitive-trip-focused metrics. The three older adult drivers had three different cognitive health statuses: one with better, relatively stable cognitive health and two with declining cognitive health associated with different cognitive assessments. The repetitive-trip-derived metrics included trip frequency, velocity metrics (mean, standard deviation, percentiles, and coefficient of variation), and route similarity. The older adult driver with better, relatively stable cognitive health had relatively stable driving patterns. The older adult driver with a marked, early decline in Trails Making B-measured cognitive health appeared to drive slower and with a higher portion of driving time spent stopped. The older adult driver with a gradual, sustained decline in MoCA-measured cognitive health had gradual changes in driving behaviours across the five-year period related to frequency, velocity, and route similarity. Therefore, this study provides a preliminary indication that repetitive-trips may provide a useful measure of older adult driving performance related to cognitive health status by reducing sources of variability. Future work is needed to assess these initial findings on a larger sample of older adult drivers.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".