Using confidence intervals to compare several correlated areas under the receiver operating characteristic curves
Why this work is in the frame
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Bibliographic record
Abstract
The performance of a diagnostic tool yielding quantitative or ordinal measurements is often assessed in terms of its area under the receiver operating characteristic curve (AUC). As new diagnostic tools are constantly being developed, a frequently occurring task is to compare multiple AUCs as derived from the same group of subjects. For this purpose, previous methods have usually used an omnibus chi-square test, which may not be very informative. We present here methods for comparing several correlated AUCs using simultaneous confidence intervals. To improve small sample properties, we adopt the method of variance estimates recovery in which confidence limits for each AUC are obtained on the basis of the logit and inverse hyperbolic sine transformations. A simulation study demonstrates the superior performance of the proposed approach. The methods are illustrated with two examples.
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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 | 0.004 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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