Bovine Reproductive Palpation Training: Does the Cow Make a Difference?
Bibliographic record
Abstract
Gaining experience and dexterity for trans-rectal cattle palpation requires substantial training. Simulation allows students to perform palpation without risks and to obtain feedback, but many believe live cattle palpation is essential. Limited research exists on the proper training method for live animal trans-rectal palpation. This study compared student improvement in laboratory palpation skills when assigned to the same cows versus choosing a cow at random. The hypothesis for the study was that students assigned the same cow, as compared to students choosing a cow at random, would be more accurate at palpation, would learn what structures are present on the ovaries and what size the reproductive tract measures, and would be able to follow the cyclicity of the cow. Cervical diameter, uterine tone, diameter of left and right uterine horns, and ovarian structures were recorded over time. Responses were compared to laboratory instructors' responses and Z-tests for proportions were used to test the differences in percentage correct at each time point for each palpation exercise. Overall the experiment showed that assigning students to certain cows will not improve their trans-rectal palpation training. However, asking students to identify specific landmarks with quantitative measurements did allow for more productive laboratory time and engaged students. The results of the present study also suggest that if there is limited time available for palpation instruction, choosing cows with behavior allowing easy handling is important to the educational process.
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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 | 0.004 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".