Utility of an online learning module to teach cautery disbudding technique for dairy calves, including cornual nerve block application
Bibliographic record
Abstract
Although disbudding or dehorning dairy heifers is necessary for the safety of humans and other cattle, it has been identified as a key animal welfare issue when done without appropriate analgesia. Three-quarters of all disbudding or dehorning is done by dairy producers or on-farm staff, while the remainder is done by a veterinarian or veterinary technician. Reported use of pain control for these procedures by dairy producers ranges from 15 to 60%. Cautery disbudding is the most commonly used method; best practices include administration of a non-steroidal anti-inflammatory drug (NSAID) as well as local anesthetic given as a cornual nerve block (CNB). While NSAID administration is uncomplicated, CNB application requires technical training, which may limit use. Teaching methods have traditionally focused on one-on-one training with a veterinarian, although online disbudding training videos exist. To our knowledge, neither method has been studied for efficacy. Our objective was to determine if an online, interactive module could teach naive participants cautery disbudding technique, including CNB, as compared to hands-on training.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".