Orthopedic applications of 3D printing in canine veterinary medicine
Bibliographic record
Abstract
Objective: This case series investigates the application of 3D printing in veterinary orthopedic surgeries, emphasizing its potential to enhance preoperative planning, intraoperative precision, and postoperative outcomes. Animals: Three canines-German Shepherd, Basset Hound, and Labrador Retriever-were included in this study. Materials and methods: Three canine cases involving complex orthopedic deformities were selected to illustrate different uses of 3D printing in veterinary surgery. CT scans were segmented using Materialise Mimics 26.0, followed by virtual surgical planning and creation of 3D printed models and guides. Results: In Case 1, a 2-year-old German Shepherd with a congenital right tibial deformity underwent successful surgical correction aided by a preoperatively prepared external fixator frame, saving approximately 1 h of OR time. In Case 2, a 1-year-old Basset Hound with a left antebrachial deformity had a double wedge osteotomy performed with the assistance of patient-specific cutting and reconstruction guides, leading to optimal alignment and reduced surgical time. Case 3 involved a young, less than 1-year-old Labrador Retriever rescue with severe bilateral tibiofemoral deformity, where 3D printed models helped the surgeon determine that surgery was not the best option, potentially preventing a poor outcome. Clinical relevance: This case series highlights the transformative potential of 3D printing in veterinary orthopedic surgery, illustrating its ability to improve aid surgical outcomes, reduce operative times, and be a valuable tool in preoperative decision-making. This technology allows for tailored surgical interventions, enhancing the precision and effectiveness of treatment plans in veterinary medicine.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".