A Planned Multidisciplinary Surgical Approach to Treat Primary Pelvic Malignancies
Bibliographic record
Abstract
The pelvic anatomy poses great challenges to orthopedic surgeons. Sarcomas are often large in size and typically enclosed in the narrow confines of the pelvis with the close proximity of vital structures. The aim of this study is to report a systematic planned multidisciplinary surgical approach to treat pelvic sarcomas. Seventeen patients affected by bone and soft tissue sarcomas of the pelvis, treated using a planned multidisciplinary surgical approach, combining the expertise of orthopedic oncology and other surgeons (colleagues from urology, vascular surgery, abdominal surgery, gynecology and plastic surgery), were included. Seven patients were treated with hindquarter amputation; 10 patients underwent excision of the tumor. Reconstruction of bone defects was conducted in six patients with a custom-made 3D-printed pelvic prosthesis. Thirteen patients experienced at least one complication. Well-organized multidisciplinary collaborations between each subspecialty are the cornerstone for the management of patients affected by pelvic sarcomas, which should be conducted in specialized centers. A multidisciplinary surgical approach is of paramount importance in order to obtain the best successful surgical results and adequate margins for achieving acceptable outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".