Influence of an Exercise Program, Muscle Strength, Proprioception, and Arm Length on Veterinary Students’ Bovine Pregnancy Diagnosis Accuracy
Bibliographic record
Abstract
Bovine pregnancy diagnosis (PD) by transrectal palpation (TRP) is an important skill for veterinary graduates. Factors influencing students’ PD accuracy were investigated to optimize bovine PD by TRP training without increasing live animal exposure. The objective was to determine whether arm length and strength, proprioception, and exposure to a 6-week exercise training program were significantly associated with students’ PD accuracy. Veterinary students ( n = 128) who had previously received formal theoretical and practical training in bovine TRP and PD (live cows and TRP simulators) were assessed for PD accuracy on live cows. Prior to assessment, arm muscle strength measurement, an exercise program, and additional TRP sessions on Breed’n Betsy® simulators and live cows were offered to the students. Seventy-eight students volunteered to participate in the arm length measurement, muscle strength, and proprioception testing. Of these, 35 randomly allocated students completed a 6-week exercise program, after which muscle strength was reassessed. Each student performed PDs on six cows of which the pregnancy status, ranging from 6 weeks to 9 months pregnant or not pregnant, was predetermined by an experienced veterinarian. PD accuracy was measured as sensitivity and specificity, being defined as the proportion of pregnant or nonpregnant cows, respectively, correctly identified by the student. It is concluded that hand grip strength and participation in an exercise program are significant predictors of veterinary students’ PD accuracy. Implementation of an exercise program aimed at improving grip strength in the veterinary curriculum is a novel approach to improve bovine TRP and PD 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 | 0.007 |
| 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 | 0.000 |
| 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".