Three-Dimension-Printed Custom-Made Prosthetic Reconstructions in Bone Tumors: A Single Center Experience
Bibliographic record
Abstract
Bone can be affected by different neoplastic conditions. Limb salvage surgery has become the preferred treatment strategy for most malignant tumors of the extremities. Advanced 3D printing technology has transformed the conventional view of oncological surgery. These types of implants are produced by electron beam melting (EBM) technology by sintering titanium powder in a scaffold shape designed following a project designed from HRCT and MRI. The aim of our study was to evaluate the outcomes and the mid-term follow-up of a population treated with 3D-printed custom-made prosthesis implantation in major oncological bone resection or after failure of primary implants. The primary outcome was the general patient satisfaction one year after surgery. The secondary outcomes were: mortality rate, treatment related complication rate, functional and clinical outcomes (KPS, ADL and IADL). Eight patients were included, five females and two males, with a mean age of 50.3 (±23.72) years at the surgery. The enrolled patients reported a mean satisfaction rate after surgery of 7.38 (±2) where 10 was the maximum value. There were no changes between pre- and postoperative mean KPS (81.43 +/−10.69). Mean preoperative ADL and IADL score was in both cases 4.86 (±1.07), while postoperative was 5 (±0.82), with a delta of 0.13 (p > 0.05). Custom-made prosthesis permits reconstructing bone defects caused by large tumor resection, especially in anatomically complex areas, restoring articular function.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".