Personalized 3D-printed endoprostheses for limb sparing in dogs: Modeling and <i>in vitro</i> testing
Bibliographic record
Abstract
Osteosarcoma is the most common type of bone cancer in dogs, treatable by amputation or limb-sparing surgery. For the latter, commercially available plate - endoprosthesis assemblies require contouring, to be adapted to the patient's bone geometry, and lead to sub-optimal results. The use of additively-manufactured personalized endoprostheses and cutting guides for distal radius limb-sparing surgery in dogs presents a promising alternative. Specialized software is used for the bone structure reconstruction from the patient's CT scans and for the design of endoprostheses and cutting guides. The prostheses are manufactured from a titanium alloy using a laser powder bed fusion system, while the cutting guides are manufactured from an ABS plastic using a fused deposition modeling system. A finite element model of an instrumented limb was developed and validated using experimental testing of a cadaveric limb implanted with a personalized endoprosthesis. Personalized endoprostheses and cutting guides can reduce limb sparing surgery time by 25-50% and may reduce the risk of implant failure. The numerical model was validated using the kinematics and force-displacement diagrams of the implant-limb construct. The model indicated that a modulus of elasticity of an implant material ranging from 25 to 50 GPa would improve the stress distribution within the implant. The results of the current study will allow optimization of the design of the personal implants in both veterinary and human patients.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".