Efficacy of automated in-vehicle technologies to improve driver fitness of people with Parkinson’s Disease
Bibliographic record
Abstract
The objective of this study is to measure how In-Vehicle Information Systems (IVIS) and Advanced Driver Assistance Systems (ADAS) impact the driving abilities of individuals with Parkinson's Disease (PD), specifically by quantifying the number of on-road errors. In the experiment, 107 PD participants drove a 2019 Toyota Camry on designated highway and suburban roads. Participants alternated between driving half of the route with IVIS and ADAS activated and the other half with the systems deactivated. Driving performance data, including on-road errors, were collected by both the in-vehicle Driver Rehabilitation Specialist (DRS) and satellite telemetry. Some technical issues were encountered during the data collection process, resulting in some incomplete or missing data points. These entries were left vacant in the datasheet file (Processed and Summary of IVIS-ADAS Activation Data File), and a note column was added to denote the issue associated with the relevant variables. Here is the description of each data file: 1. Demographic Data File: Surveys data, including Recruiting Phone Screening Survey, Demographic Questionnaire, Montreal Cognitive Assessment (MoCA), Modified Hoehn and Yahr scale, Movement Disorders Society Unified Parkinson's Disease Survey (MSD-UPDPS), UPPS-P Impulsivity Scale, optec2500 Visual Screening, Snellen eye chart, Technology Readiness Survey, Automated Vehicle Survey, and Parkinson's Disease Questionnaire. 2. Driving Errors Data (Driver Rehabilitation Specialist) File: Driving errors recorded by the DRS during the experimental drive on suburban and highway roads. 3. Processed and Summary of IVIS-ADAS Activation Data File: Driving errors and performance data captured by the telemetry system during the experimental drive on suburban and highway roads, processed by a human researcher. 4. Data Dictionary File: Description of all variables in the three datasheet files.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".