How to Report and Interpret Screening Test Properties: Guidelines for Driving Researchers
Bibliographic record
Abstract
One important goal of driving research is the development of a short but valid office-based screening test for fitness to drive of aging drivers. Several candidate tests have been proposed already, and no doubt others will be proposed in the future. It might seem obvious that authors advocating for the adoption of a particular screening test or procedure should report sensitivity, specificity, and other common screening test properties. Unfortunately, driving researchers have frequently failed to report any screening test properties. Others have reported screening test properties but have made basic mistakes such as calculating predictive values of positive and negative tests but reporting them incorrectly as sensitivity and specificity. These omissions and errors suggest that some driving researchers may be unaware of the importance of accurately reporting test properties when proposing a screening procedure and that others may need a refresher on how to calculate and interpret the most common screening test properties. Many good learning resources for screening and diagnostic tests are available, but most of them are intended for students and researchers in medicine, epidemiology, or public health. We hope that this tutorial in a prominent transportation journal will help lead to improved reporting and interpretation of screening test properties in articles that assess the usefulness of potential screening tools for fitness to drive.
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How this classification was reachedexpand
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".