Inter-Rater Reliability of Scoring Systems for Abomasal Lesions in Quebec Veal Calves
Bibliographic record
Abstract
The objective of this study was to determine the inter-rater reliability of current scoring systems used to detect abomasal lesions in veal calves. In addition, macroscopic lesions were compared with corresponding histological lesions. For this, 76 abomasa were retrieved from veal calves in a slaughterhouse in Quebec and scored by four independent raters using current scoring systems. The localisations of the lesions were separated into pyloric, fundic, or torus pyloricus areas. Lesions were classified into three different types, i.e., erosions, ulcers, and scars. To estimate the inter-rater reliability, the coefficient type 1 of Gwet’s agreement and Fleiss κ were used for the presence or absence of a lesion, and the intra-class correlation coefficient was used for the number of lesions. All veal calves had at least one abomasal lesion detected. Most lesions were erosions, and most of them were located in the pyloric area. Overall, a poor to very good inter-rater agreement was seen for the pyloric area and the torus pyloricus regarding the presence or absence of a lesion (Fleiss κ: 0.00–0.34; Gwet’s AC1: 0.12–0.83), although a higher agreement was observed when combining all lesions in the pyloric area (Fleiss κ: 0.09–0.12; Gwet’s AC1: 0.43–0.93). For the fundic area, a poor to very good agreement was also observed (Fleiss κ: 0.17–0.70; Gwet’s AC1: 0.90–0.97). Regarding the inter-rater agreement for the number of lesions, a poor to moderate agreement was found (ICC: 0.11–0.73). When using the scoring system developed in the European Welfare Quality Protocol, a poor single random rater agreement (ICC: 0.42; 95% CI: 0.31–0.56) but acceptable average random rater agreement (ICC: 0.75; 95% CI: 0.64–0.83) was determined. Microscopic scar lesions were often mistaken as ulcers macroscopically. These results show that the scoring of abomasal lesions is challenging and highlight the need for a reliable scoring system. A fast, simple, and reliable scoring system would allow for large scale studies which investigate possible risk factors and hopefully help to prevent these lesions, which can compromise veal calves’ health and welfare.
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How this classification was reachedexpand
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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".