“In-house” Design and Use of 3-dimensional Printed Patient-specific Bone Tumor Resection Guides for Geometric Osteotomies in Sarcoma Surgery
Bibliographic record
Abstract
Three-dimensional printing technology has rapidly advanced as a promising technology for preoperative planning, education, and surgical execution in orthopedic surgery. Use of patient-specific instrumentation in orthopedic oncology sarcoma cases can streamline complex osteotomies while providing safe margins based on predetermined osteotomy levels. We describe use of an “in-house” protocol to create patient-specific bone tumor resection guides for use in orthopedic oncology cases. The described protocol bypasses expensive outsourcing options and facilitates use of preoperative surgical simulation and intimate involvement of the surgical team in the guide design. We report on the successful design and use of three-dimensional printed patient-specific bone tissue resection guides in a case of proximal tibial parosteal osteosarcoma resection and reconstruction with a size-matched allograft, and in a case of a secondary pelvic chondrosarcoma resection.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".