Teaching Transrectal Palpation of the Internal Genital Organs in Cattle
Bibliographic record
Abstract
In this article, a simulation model for rectal palpation teaching in cows, Breed'n Betsy, is evaluated. Furthermore, the learning process of rectal palpation is depicted during a training period in live cows. In experiment 1, eight students were trained in live cows (group A) and nine students were trained using Breed'n Betsy (group B). After 25 palpations, their ability to localize and evaluate structures was evaluated in practical tests in live cows. Group A had higher results than group B (p<0.001) and were more skilled at localizing the uterus and localizing and evaluating the ovaries (p<0.05). Group B was better at pregnancy diagnosis (nonsignificant). Results suggest that Breed'n Betsy cannot fully replace training in live cows, but may be a valuable addition to the classical teaching method. Suggestions for future improvement are made. In experiment 2, 10 students were intensely trained in live cows throughout the year and evaluated in practical tests at three time points (September, January, and March). Results were analyzed as a function of time point and the category of experience (1: 0-50 cows; 2: 50-100 cows; 3: 100-150 cows; 4: 150-200 cows; 5: >200 cows). Results increased in time (p<0.05) and were higher in categories 3, 4, and 5 than in category 1 (p<0.05). Although all of the students in the higher categories successfully localized the cervix, uterus, and ovaries, they had difficulties in interpreting these structures, suggesting that palpation of 200 cows is insufficient to reach a consistent level of expertise.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".