Joint estimation of multiple disease‐specific sensitivities and specificities via crossed random effects models for correlated reader‐based diagnostic data: application of data cloning
Bibliographic record
Abstract
We present a model for describing correlated binocular data from reader-based diagnostic studies, where the same group of readers evaluates the presence or absence of certain diseases on binocular organs (e.g., fellow eyes) of patients. Multiple random effects are incorporated to meaningfully delineate various associations in the data including crossed random effects to account for reader-specific variability and to incorporate cross correlations. To overcome the computational complexity involved in the evaluation and maximization of the marginal likelihood, we adopt the data cloning approach, which calculates maximum likelihood estimates under the Bayesian paradigm. The bias and efficiency of the estimates are assessed in two simulation studies. We apply our model to data from a diabetic retinopathy study.
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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 | 0.071 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".