Variables Affecting Veterinary Students’ Ability to Accurately Interpret Ovulation in Live Mare Palpation
Bibliographic record
Abstract
In a veterinary medicine curriculum, students’ hands-on practice is essential but is still considered one of the major deficiencies in veterinary schools in Europe. After theoretical and basic practical training, students, under the control of experienced veterinarians (supervisors), monitored the reproductive cycle of embryo recipients by transrectal palpation and ultrasound. To evaluate the skills of students, the question “Has she ovulated?” was posed when a dominant follicle ≥ 35 mm was recorded in the previous day’s examination and a score of 1 or 0 was assigned in the case of a correct or incorrect answer (test palpation), respectively. Study 1 involved the retrospective evaluation of 3,509 test palpation records of 43 students (31 females, 12 males) and showed a statistically significant positive correlation between the number of test palpations performed and the proportion of correct answers. There was a statistically significant effect of the number of test palpations performed by each student, their gender, and the season on the correct answers. When performing > 50 test palpations, a statistical difference between gender was observed ( p < .05). Study 2 involved the prospective evaluation of 687 records on 52 standardbred or thoroughbred recipient mares collected from nine right-handed female students. The different mares, breed, occurrence of ovulation on the left or right ovary, and the presence of one or more large follicle(s) per ovary had no effect on the correct answers ( p > .05). Individual students’ performances were statistically different ( p < .05), ranging from 60% to 92%.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".