Collagen Membranes for Host-Implant Integration: A Pilot Clinical Study
Bibliographic record
Abstract
PURPOSE: To evaluate host-implant integration with collagen membranes in 14 patients who underwent limb salvage surgery for musculoskeletal oncological disease. METHODS: 8 females and 6 males aged 10 to 69 (mean, 30) years underwent limb savage surgery with collagen membranes (Tutomesh; Tutogen Medical, Germany) for osteosarcoma (n=7), chondrosarcoma (n=3), giant cell tumour (n=1), malignant fibrous histiocytoma (n=1), arteriovenous malformation (n=1), and pigmented villonodular synovitis (n=1). The procedures performed were proximal humeral resection (n=3), partial scapulectomy (n=1), proximal femoral resection (n=2), total femoral resection (n=2), proximal tibial resection (n=3), and wide resection of soft tissues of the knee (n=3). In addition, 10 patients underwent endoprosthesis reconstruction. Reconstruction of musculoskeletal defects was classified into type I (intercalary, n=2), type II (joint, n=4), and type III (both, n=8). Graft incorporation and local recurrence were monitored. Clinical outcome measures entailed the Short Form-36, Toronto Extremity Salvage Score (TESS), and Musculoskeletal Tumor Society Score (MSTS). RESULTS: Two patients with proximal tibial resection and one with total femoral resection had wound healing problems. No patient had any infection or any foreign body reaction necessitating implant removal. Eight patients with type II or III reconstruction were followed up for a mean of 11 (range, 1-23) months. Their scores in the Short Form-36, TESS, and MSTS were similar to those who had undergone reconstructions without the membrane, with the exception of type II reconstructions for which the membrane conferred good results. CONCLUSION: The Tutomesh membrane facilitated host-implant integration and provided a feasible anatomic reconstruction for ligaments in the shoulder, knee, and hip.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".