What to Teach in Small Animal Veterinary Orthopedics: A Survey of Practicing Veterinarians to Inform Curriculum Development
Bibliographic record
Abstract
Competency-based veterinary education focuses on the knowledge and clinical skills required to generate a productive and confident practitioner. Accurate identification of clinically relevant core competencies enables academic institutions to prioritize which new and foundational information to cover in the limited time available. The goal of this study was to aggregate the opinions of veterinary practitioners about small animal core competencies in veterinary orthopedics. An online 20-question survey was distributed with questions regarding respondent demographics, education, practice type, caseload, involvement in orthopedic procedures, access to referral hospitals, frequency of orthopedic condition presentation and procedure performance, and proposed percent allocation of various orthopedic curriculum topics. Responses were included from 721 respondents, largely first-opinion veterinarians (81%, n = 580/721). The majority (58%; n = 418/721) of respondents performed less than 10% of the orthopedic surgeries themselves and, 37% ( n = 266/721) reported never performing orthopedic surgery; of those performing surgeries, 78% ( n = 354/455) performed less than six orthopedic procedures monthly. The five most common orthopedic conditions seen included generalized osteoarthritis, patellar luxation, cranial cruciate ligament disease, hip dysplasia/arthritis, and muscle/tendon injuries. Median respondent scores for the percentage that a topic should compose in an ideal orthopedic curriculum were 20% each for “orthopedic exam” and for “non-surgical orthopedic knowledge,” 15% each for “non-surgical orthopedic skills,” “orthopedic imaging (radiographs),” and “surgical orthopedic knowledge,” 10% for “surgical orthopedic skills,” and 2% for “advanced orthopedic imaging.” Based on these results, a curriculum focusing on the most clinically relevant orthopedic conditions with an emphasis on diagnosis establishment and non-surgical treatments is proposed.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".