Concerns about composite reference standards in diagnostic research
Bibliographic record
Abstract
Composite reference standards are used to evaluate the accuracy of a new test in the absence of a perfect reference test. A composite reference standard defines a fixed, transparent rule to classify subjects into disease positive and disease negative groups based on existing imperfect tests. The accuracy of the composite reference standard itself has received limited attention. We show that increasing the number of tests used to define a composite reference standard can worsen its accuracy, leading to underestimation or overestimation of the new test’s accuracy. Further, estimates based on composite reference standards vary with disease prevalence, indicating that they may not be comparable across studies. These problems can be attributed to the fact that composite reference standards make a simplistic classification and then ignore the uncertainty in this classification. Latent class models that adjust for the accuracy of the different imperfect tests and the dependence between them should be pursued to make better use of data
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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 | 0.777 | 0.873 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.016 | 0.013 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".