Veterinarians’ Competence in Applying Basic Genetic Principles and Daily Implementation of Clinical Genetics: A Study in a University Environment
Bibliographic record
Abstract
Veterinarian competency in genetics is vital for a meaningful application of the rapidly growing number of genetic tests available for animals. We evaluated the use of genetic tests in the daily veterinary practice and the competency of university-employed veterinarians in applying basic principles of genetics in a clinical setting through an electronic survey with 14 cases and 7 statements on genetics. Ninety-one non-geneticist veterinarians from two veterinary faculties in two different countries responded. Almost half of the participants apply genetic tests during their daily work, with frequencies varying between weekly and once a year. The most common indication to request a genetic test was diagnostic testing of clinically ill patients. Although 80% of the veterinarians communicated the result of a genetic test themselves, only 56% of them found it "very to rather easy" to find the correct test, and only 32% of them always felt competent to interpret the result of the test. The number of correctly answered questions varied widely, with median scores of 9/14 (range 0-14) and 5/7 (range 0-7) for the cases and statements, respectively. Most difficulties were seen with recognition of pedigree inheritance patterns, while veterinarians scored better in breeding advice and probability of disease estimations. Veterinarians scored best on questions related to autosomal recessive inheritance, followed by complex, autosomal dominant, X-linked recessive, and X-linked dominant inheritance. This study exposed pain points in veterinarians' knowledge and has led to the formulation of recommendations for future education and communication between laboratories, geneticists, and veterinarians.
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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 | 0.002 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".