Reliability analysis for continuous measurements: Equivalence test for agreement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In tandem with the rapid development of medical technology, methods for assessing intrarater and interrater reliability or agreement across tools for continuous measurements have become an increasingly important research topic. Thus far, a number of reliability assessment methods have been proposed. Among them, the limits of agreement and repeatability coefficients were found to be the most useful tools for assessing reliability when measurements are on a continuous scale. However, both are considered as descriptive methods. The concepts of consistency or conformity require an equivalence test without which judgment would be subjective. In this paper we will extend the repeatability coefficient approach and propose an equivalence test that can be used to confirm the agreement between two or more measurement tools or assess interrater and intrarater reliability. Using this approach, a formula to calculate sample size will also be suggested and examples will be provided to illustrate the method.
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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 | 0.052 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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