Estimating error rates in the classification of paired organs
Bibliographic record
Abstract
Clinical data from paired organs present a dependence structure that has to be considered when making statistical inference or evaluating classification rules with resampling-based techniques (bootstrap, cross-validation). We introduce a paired cross-validation approach for the estimation of misclassification error rates in the classification of data from paired organs. The dependence structure of the sample is honored by subject-level cross-validation. Theoretical considerations as well as a case-control study on glaucoma diagnosis and a simulation study show that the variance of the paired cross-validation estimator is considerably lower than in traditional cross-validation error estimation on one randomly selected eye per subject. The actual variance reduction is mainly controlled by the contribution of differential misclassification between both eyes to the overall error rate. By contrast, 'ad hoc' cross-validation ignoring the autocorrelation of paired organs leads to biased error estimates. Using the double-bagging technique, we also show that classification accuracy can be improved by using information from both eyes in training machine-learning classifiers. In glaucoma detection, the reduction in misclassification error rates by training data from both eyes is equivalent to an increase in the sample size by one-third to one-half, which is an important achievement in clinical studies.
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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 | 0.059 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".