An Empirical Comparative Assessment of Inter-Rater Agreement of Binary Outcomes and Multiple Raters
Why this work is in the frame
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Bibliographic record
Abstract
Background: Many methods under the umbrella of inter-rater agreement (IRA) have been proposed to evaluate how well two or more medical experts agree on a set of outcomes. The objective of this work was to assess key IRA statistics in the context of multiple raters with binary outcomes. Methods: We simulated the responses of several raters (2–5) with 20, 50, 300, and 500 observations. For each combination of raters and observations, we estimated the expected value and variance of four commonly used inter-rater agreement statistics (Fleiss’ Kappa, Light’s Kappa, Conger’s Kappa, and Gwet’s AC1). Results: In the case of equal outcome prevalence (symmetric), the estimated expected values of all four statistics were equal. In the asymmetric case, only the estimated expected values of the three Kappa statistics were equal. In the symmetric case, Fleiss’ Kappa yielded a higher estimated variance than the other three statistics. In the asymmetric case, Gwet’s AC1 yielded a lower estimated variance than the three Kappa statistics for each scenario. Conclusion: Since the population-level prevalence of a set of outcomes may not be known a priori, Gwet’s AC1 statistic should be favored over the three Kappa statistics. For meaningful direct comparisons between IRA measures, transformations between statistics should be conducted.
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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 | 0.004 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it