Predicting Older Driver On-Road Performance by Means of the Useful Field of View and Trail Making Test Part B
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Useful Field of View(®) (UFOV) and Trail Making Test Part B (Trails B) are measures of divided attention. We determined which measure was more accurate in predicting on-road outcomes among drivers (N = 198, mean age = 73.86, standard deviation = 6.05). Receiver operating characteristic curves for the UFOV (Risk Index [RI] and Subtests 1-3) and Trails B significantly predicted on-road outcomes. Contrasting Trails B with the UFOV RI and subtests, the only difference was found between the UFOV RI and Trails B, indicating the UFOV RI was the best predictor of on-road outcomes. Misclassifications of drivers totaled 28 for the UFOV RI, 62 for Trails B, and 58 for UFOV Subtest 2. The UFOV RI is a superior test in predicting on-road outcomes, but the Trails B has acceptable accuracy and is comparable to the other UFOV subtests.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it