Electromyographic Analysis of Muscle Activation Patterns During Bovine Transrectal Palpation and the Development of the Bovine Pregnancy Diagnosis Improvement Exercise Program
Bibliographic record
Abstract
The unusual and tiring physical activity of bovine transrectal palpation (TRP) requires a novel approach to improve students’ TRP and pregnancy diagnosis (PD) skills. It has been shown that students who participated in an exercise program and students who had a grip strength (GS) of more than 30 kilograms performed better in bovine PDs. Participation in the exercise program increased students’ sensitivity (ability to identify pregnant cows) but did not increase total arm muscle strength. To identify which muscles are used during TRPs and to improve the exercise program, an electromyographic (EMG) analysis was used to identify muscle activation patterns and muscle activity levels during bovine TRPs. Eight subject matter experts (SMEs) each palpated two live cows and one Breed’n Betsy ® rectal examination simulator while an EMG Triggered Stimulator recorded muscle activity. Muscle activation was higher for forearm muscles compared with all other examined muscle groups ( p < .001); was higher during retraction of the uterus and palpation of left and right uterine horn, compared with palpation of cervix, uterine body, left ovary, and right ovary ( p < .001); and showed an endurance pattern. Findings have been used to modify the previously developed exercise program in effort to improve students’ TRP and PD skills. The Bovine PD Improvement Exercise Program is available to students through an online application (http://icarus.up.ac.za/vetmlp/) and aims to not only improve GS and TRP accuracy but also stamina and well-being while adding fun to busy study schedules.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".