On the Equivalence of Classic ROC Analysis and the Loss-function Model to Set Cut Points in Sequential Testing
Why this work is in the frame
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Bibliographic record
Abstract
In an effort to reduce the cost of administration for objective structured clinical examinations (OSCEs), several authors have promoted the use of sequential testing in which all candidates take a short screening test and candidates who pass the screen are exempted from taking the full test. Traditionally, the determination of the optimally efficient cut point (passing score) for the screen has used ROC analysis to minimize false-positive and false-negative errors. Recently, Muijtjens et al. have questioned the appropriateness of the ROC method for these purposes and have promoted an alternative method that uses a "loss" formula. However, given certain theoretically derived conditions, it can be shown that the use of the loss formula is functionally identical to using ROC analysis, and the authors suggest that continued use of the ROC method is appropriate.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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