Reliability and agreement during the Rapid Entire Body Assessment: Comparing rater expertise and artificial intelligence
Bibliographic record
Abstract
The purpose of this study was to examine the reliability and agreement between human raters (novice, intermediate, and expert) and TuMeke Risk Suite when assessing work with the Rapid Entire Body Assessment (REBA). Twenty-one videos portraying veterinarians performing an equine radiograph were assessed with REBA by human raters and TuMeke Risk Suite (ergonomic artificial intelligence software). Intra-rater reliability of the final REBA score was highest for TuMeke Risk Suite (ICC = 1.0), then the expert rater (ICC = 0.89 (0.78-0.95)), and lowest for the novice rater (ICC = 0.51 (0.25-0.74)). Agreement between the expert rater and TuMeke Risk Suite was highest for scores of the trunk, leg, and upper arm, and lowest for the neck, wrist, and lower arm. The REBA tool in TuMeke Risk Suite may be of benefit to less experienced users to enhance reliability of their REBA assessments, especially when the trunk, legs, and upper arm are of primary interest.
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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 | 0.061 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".