Novel Surgical Reconstruction Using a 3D Printed Cement Mold Following Resection of a Rare Case of Proximal Ulna Osteosarcoma: A Case Report and Description of the Surgical Technique
Bibliographic record
Abstract
Osteosarcoma is one of the most common primary bone malignancies, typically occurring around the knee. However, the forearm is a rare site, with tumors in the proximal ulna being extremely uncommon. Primary sarcoma in this location presents a surgical challenge due to the complex anatomy and limited reconstructive options. We report a rare case of a 19-year-old female with non-metastatic, high-grade giant cell-rich osteosarcoma involving the right proximal ulna. To our knowledge, this is only the second reported adult case of this histological subtype in this location. The patient was treated at a specialized oncology center with neoadjuvant and adjuvant chemotherapy, along with wide intra-articular resection for local tumor control. Reconstruction was achieved using a novel, customized 3D-printed articulating cement spacer mold with plate osteosynthesis. Artificial elbow ligamentous reconstruction was performed using FiberTape and FiberWire sutures passed through drill holes, and the triceps tendon was reattached to the cement mold using an endobutton. This cost-effective and personalized surgical approach allowed successful joint reconstruction while maintaining elbow stability and function. Our case highlights a feasible reconstructive option for rare and anatomically challenging osteosarcoma presentations, contributing to the limited literature on proximal ulna giant cell-rich osteosarcoma.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 | 0.002 |
| 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".