Longitudinal changes in driving behaviours among older adults with different A/T/N biomarker profiles
Bibliographic record
Abstract
Abstract Background The pathophysiological process of Alzheimer’s disease (AD) spans approximately 10‐15 years. To define AD by its pathologic processes, the A/T/N classification framework was proposed constituting ß‐amyloid (A), tau (T), and neurodegeneration (N) biomarkers derived from cerebrospinal fluid (CSF) or imaging. With these biomarkers, individuals can be divided into four categories: normal (A‐T‐N‐), AD pathologic change (A+T‐N‐), AD (A+T+N‐, A+T‐N+, A+T+N+), and suspected non‐AD pathology (SNAP) (A‐T+N‐, A‐T‐N+, A‐T+N+). Recent evidence indicates that daily driving behaviours can identify preclinical AD, solely based on CSF ß‐amyloid (A), with high accuracy. This study aims to determine the extent to which longitudinal changes in driving behaviour vary between individuals with different A/T/N profile categories. Method Participants were enrolled in a longitudinal study on driving and preclinical AD biomarkers from Washington University School of Medicine. Temporospatial driving behaviours were collected with a data logger installed in participant vehicles over a period between January 2015 and March 2020. CSF collected within three years of each driving event was used to assign A/T/N categories. The cohort included 132 cognitively normal (Clinical Dementia Rating of 0) older adults (>65). Changes in driving behaviours over time were modelled for each group using linear mixed‐effects models. A Z‐test was used to determine if the slopes significantly differed between individuals with normal biomarkers and the remaining groups. Result Participants were classified as 59 individuals with normal biomarkers, 9 with AD pathologic change biomarkers, 45 with AD biomarkers, and 19 with SNAP biomarkers. Compared to normal aging, the AD pathology group had a statistically significantly higher increase in average occurrences of under‐speeding (p = 0.001), a significant decrease in average trip duration (p = 0.021), and a significant decrease in the average standard deviation of vehicle speed (p = 0.029). The AD group had no significant differences from the normally aging group. The SNAP group showed a significantly higher increase in the average occurrences of over‐speeding (p<0.001) than normal aging. Conclusion Driving behaviours and their rate of change over time vary between individuals with different A/T/N profile categories. These differences provide an opportunity to strengthen our understanding of the disease trajectory.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".