Limb Sparing in Dogs using Individualized 3D-Printed Endoprostheses and Cutting Guides for Distal Radial Osteosarcoma: A Pilot Study
Bibliographic record
Abstract
Introduction: Using 3D-printed “personalized” implants may reduce the risk of complications for limb sparing in dogs. A disadvantage is the time required to manufacture the implant. The goals were to assess the feasibility and outcome of using 3D-printed implants and cutting-guides in the clinical setting for dogs with distal radial osteosarcoma. Materials and Methods: Data from a CT scan of both thoracic limbs were used to manufacture a cutting-guide and endoprosthesis. Intra-arterial carboplatin was administered after the CT starting with the second dog. A second CT was repeated before surgery where limb sparing was performed. Dogs were monitored postoperatively with physical examinations and chest and limb radiographs. Results: Five dogs participated and 4 received intra-arterial carboplatin. For all dogs that received intra-arterial chemotherapy, no tumor substantially increased in dimension between initial CT and surgery. All specimens had complete margins. Four dogs had a complication: 4 had an infection and one each had a skin laceration, skin necrosis, fracture of the radius, implant pulling out of the radius, and local recurrence. Two dogs required an amputation. One dog had a survival time of 192 days. The other 4 dogs were alive with a follow-up period of 293 to 377 days. Discussion/Conclusion: 3D-printed personalized implants were successfully manufactured in the clinical setting. To allow more time between CT and surgery without the tumor getting significantly larger, intra-arterial carboplatin was administered. Intra-arterial carboplatin appears to be an effective strategy to prevent the tumor to grow excessively during the design and manufacturing periods. Complications remained common. Acknowledgment: VOI Inc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".