Investigating the autonomic nervous system and cognitive functions as potential mediators of an association between cardiovascular disease and driving performance
Bibliographic record
Abstract
Cardiovascular disease (CVD) impacts the autonomic nervous system and cognitive functions related to activities of daily living, including driving an automobile. Although CVD has been linked to unsafe driving, mechanisms underlying this relationship remain elusive. The aim of this study was to examine the role of cognitive functions and the autonomic nervous system as potential mediators of driving performance. Nineteen individuals having recently suffered a cardiac event and 16 individuals with no history of CVD completed a simulated drive using a STISIM simulator to assess driving performance. Heart rate was recorded throughout testing using a Polar RS800CX heart rate monitor, and measures of executive, orienting, and alerting functions were obtained through the Attention Network Test. We used the Baron and Kenny analysis method to assess potential mediating effects of the relationship between CVD and driving performance. Executive function was the only potential mediator investigated to be associated with driving (p < 0.01) and CVD (p < 0.05); however, it did not appear to play a mediating role (p = 0.28). These results suggest that individuals with CVD exhibit decrements in complex cognitive tasks such as driving and that further research is needed to better understand the mechanisms underlying this relationship.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".