SERIAL TRICHOTOMIZATION TO IDENTIFY UNSAFE DRIVERS: UPDATE FROM A PROSPECTIVE STUDY
Bibliographic record
Abstract
Abstract Identifying older adults who may be unsafe to drive remains a difficult task. Gibbons et al. (2017, American Journal of Occupational Therapy) used serial trichotomization based on five cognitive tests to determine if drivers should: 1) continue driving, 2) undergo further evaluation, or 3) stop driving. The tests included the Trail-making-tests A and B (TMT-A/B), the clock-drawing test (CDT), the Motor-Free Visual Perception Test (MVPT), and the Montreal Cognitive Assessment (MoCA). Gibbons et al. relied on dual cut-off values to achieve 100% sensitivity and specificity (within their sample) to reduce false positives and false negatives that arise from using these tests in stand-alone fashion. We used the Gibbons et al. cut-off values prospectively on a cohort of 293 drivers (mean age = 66, SD = 14) referred for driving evaluations at a chronic care and rehabilitation hospital. Each driver completed the five tests. Trained occupational therapists (OTs) provided a recommendation to continue driving, undergo further evaluation, or stop driving. We examined congruence between the tests and the OTs recommendations. Weighted Kappas ranged from a low of .03 (95% CI = -.01 to .08) for the CDT, to a high of .53 (95% CI = .45 to .61) for the TMT-B. Using the same cut-offs, and serial trichotomization, the congruence with the final recommendations was moderate (k = .57, 95% CI = .49 to .66). These results remind us of the variability inherent in stand-alone cognitive tests and even within a serial trichotomization framework.
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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 | 0.026 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".