The Bateman-Type Soft Tissue Reconstruction around Proximal or Total Humeral Megaprostheses in Patients with Primary Malignant Bone Tumors—Functional Outcome and Endoprosthetic Complications
Bibliographic record
Abstract
We aimed to evaluate the functional outcome and endoprosthetic complications following the Bateman-type soft tissue reconstruction around proximal or total humeral replacements in patients undergoing resection of a primary malignant bone tumor. Between September 2001 and December 2018, a total of 102 patients underwent resection of a primary malignant bone tumor and subsequent reconstruction with a modular humeral megaprosthesis in our department. Fifteen (15%) of these patients underwent a Bateman-type soft tissue reconstruction and were included in this retrospective study. The median Musculoskeletal Tumor Society (MSTS) score was 21, the median Toronto Extremity Salvage Score (TESS) was 70, and the median American Shoulder and Elbow Surgeons (ASES) score was 72. Fifty-three percent (8/15) of all patients required a revision surgery after a median time of 6 months. There were 2 soft tissue failures, 3 infections and 3 tumor recurrences. The revision-free implant survivorship amounted to 53% (95% confidence interval (CI) 28-81) after 1 year and 47% (95% CI 22-73) at last follow-up. The Bateman-type reconstruction is a feasible option for soft tissue reconstruction but functional outcome is overall limited and the risk for revision surgery within the first postoperative year is high.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".