Development and Efficacy of a Canine Pelvic Limb Model Used to Teach the Cranial Drawer and Tibial Compression Tests in the Stifle Joint
Bibliographic record
Abstract
Canine cranial cruciate ligament rupture (CCLR) is the most common cause of pelvic limb lameness in dogs. CCLR results in palpable stifle instability secondary to cranial translation of the tibial relative to the femur, and it can be diagnosed during the orthopedic exam using the cranial drawer test (CDT) and tibial compression test (TCT). Accurate diagnosis of CCLR depends on the efficacy in performing these tests. In this study, two three-dimensional canine pelvic limb models were developed: one simulating a normal stifle and one simulating CCLR. Thirty-eight veterinary student participants answered questionnaires and performed both the CDT and TCT on a randomly assigned model. Twenty-one participants also manipulated the models one week later to assess skill retention in the short term. Mean levels of reported confidence in diagnosing CCLR and finding anatomic landmarks for CDT/TCT were significantly higher following model manipulation. Nearly all participants reported that they desired a model for teaching the diagnosis of CCLR. Most participants (92.5%) felt that the tested model would be useful for teaching CCLR diagnosis. Accuracy in diagnosing CCLR with the TCT significantly improved over time. Participant response indicated that while the tested model was effective and desirable, an ideal model would be more durable and lifelike. Further studies are needed to evaluate the developed models' effectiveness for teaching CCLR diagnosis compared to traditional teaching methods.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".