A comparison of two diagnostic performance measures
Bibliographic record
Abstract
PURPOSE: This paper aims to compare two diagnostic performance measures, i.e. signal-to-noise ratio (S/N ratio) and partial area under receiver operating characteristic curves (pAUC). It proposes the use of S/N ratio rather than pAUC for establishing optimal cut-off point for diagnostic biomarkers. DESIGN/METHODOLOGY/APPROACH: This paper discusses the properties, uses, advantages and shortcomings of the two performance measures, namely the partial area under receiver operating characteristic curve (pAUC) and Taguchi's signal-to-noise (S/N) ratio. The benefits of S/N ratio have been illustrated in a sample of four biomarkers, each having five cut-off points. The S/N ratio is compared to the pAUC index. The SAS software is employed to calculate pAUC and AUC. FINDINGS: This paper shows that S/N ratio can be used as a measure of diagnostic accuracy. The cut-off point with the highest S/N ratio is the optimal cut-off point for the biomarker. The proposed method has the advantages of being easier, more practical and less costly than that of pAUC. PRACTICAL IMPLICATIONS: This paper includes implications for the development of a more practical, equally powerful and less costly means of measuring clinical accuracy thereby reducing the costs and risks resulting from wrong selection of cut-off point can be decreased. ORIGINALITY/VALUE: This paper supports suggestions in the recent literature to replace pAUC with a new, more meaningful index.
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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 | 0.010 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".