Soft Tissue Reconstruction and Integration to Implant After Bone-Tumor Resection: A Current Concept Review
Bibliographic record
Abstract
INTRODUCTION: With the advancements in chemotherapy for malignant bone tumors, the number of patients eligible for limb salvage surgery has increased. Surgeons face a subsequent challenge in limb-sparing resection due to the need for reconstructing soft tissue coverage. The aim of this review is to focus on the present state of the field in these areas, highlighting recent advancements. METHODS: A literature research was conducted using keywords such as "soft tissue", "integration", "reconstruction", "megaprosthesis", and "soft tissue coverage", on different databases, and following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria, a total of 35 studies were selected. RESULTS: In recent times, there has been a growing emphasis on different techniques such mesh application, allograft-prosthesis composites, allograft reconstruction, a polyethylene terephthalate (PET) tube, prosthesis itself and certain metals utilized for implant coatings are used in soft tissue reconstruction. CONCLUSION: While tissue-engineered constructs and advancements in biological and cellular approaches have shown potential for enhancing osseointegration and interactions with soft tissues and implants, the actual clinical outcomes have frequently fallen short of expectations. The success of soft tissue integration is crucial for achieving functional outcomes, minimizing complications, and ensuring the long-term stability of orthopedic implants.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".