Binocular sensitivity and specificity of screening tests in cross‐sectional diagnostic studies of paired organs
Bibliographic record
Abstract
We introduce new binocular accuracy measures as alternatives to conventional marginal measures that can be used to evaluate screening tests in diagnostic studies involving paired organs (e.g. eyes and ears). Specifically, we consider screening studies based on a cross-sectional design, where both diagnosis and disease status are determined after study enrolment or sampling, yielding paired binocular binary data described via two models, namely, the extended common correlation model and the Gaussian copula probit model. The first relies on the assumption of exchangeability of fellow organs, while the second is more flexible. Binocular versions of sensitivity and specificity are defined, respectively, as the probability of at least one correct positive diagnosis in patients with one or both organs truly diseased and the probability of two correct negative diagnoses for patients with both organs truly un-diseased. Comparisons between the conventional marginal and binocular sensitivities and specificities are illustrated for both models using data from a diabetic retinopathy study. We show that our methodology provides a viable alternative to conventional ways of assessing diagnostic accuracy of screening tests for paired organs. The binocular versions of sensitivity and specificity reflect the way screening tests are conducted in practice, and they overcome the shortcomings of conventional measures. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.043 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".