Does Thirty-Minute Standardised Training Improve the Inter-Observer Reliability of the Horse Grimace Scale (HGS)? A Case Study
Bibliographic record
Abstract
The Horse Grimace Scale (HGS) is a facial-expression-based pain coding system that enables a range of acute painful conditions in horses to be effectively identified. Using valid assessment methods to identify pain in horses is of a clear importance; however, the reliability of the assessment is highly dependent on the assessors’ ability to use it. Training of new assessors plays a critical role in underpinning reliability. The aim of the study was to evaluate whether a 30-minute standardised training program on HGS is effective at improving the agreement between observers with no horse experience and when compared to an HGS expert. Two hundred and six undergraduate students with no horse experience were recruited. Prior to any training, observers were asked to score 10 pictures of horse faces using the six Facial Action Units (FAUs) of the HGS. Then, an HGS expert provided a 30-minute face-to-face training session, including detailed descriptions and example pictures of each FAU. After training, observers scored 10 different pictures. Cohen’s k coefficient was used to determine inter-observer reliability between each observer and the expert; a paired-sample t-test was conducted to determine differences in agreement pre- and post-training. Pre-training, Cohen’s k ranged from 0.20 for tension above the eye area to 0.68 for stiffly backwards ears. Post-training, the reliability for stiffly backwards ears and orbital tightening significantly increased, reaching Cohen’s k values of 0.90 and 0.91 respectively (paired-sample t-test; p < 0.001). The results suggest that this 30-minute face-to-face training session was not sufficient to allow observers without horse experience to effectively apply HGS. However, this standardised training program could represent a starting point for a more comprehensive training program for those without horse experience in order to increase their reliably in applying HGS.
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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 | 0.029 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".