Functional Outcomes of Humeral Diaphyseal Hip Spacer in Tumor Resection: A Case Report
Bibliographic record
Abstract
BACKGROUND Aggressive surgical resection is essential for managing malignant tumors involving the proximal humerus and scapula. Reconstruction of these defects presents a substantial therapeutic challenge, with functional preservation as the primary objective. Traditionally, a simple suspension technique connecting the humeral remnant to the clavicle has been utilized. While allografts and prosthetic replacements offer potential benefits, they are often associated with serious complications, such as infection, resorption or dislocation. This study aimed to assess the outcomes of a novel surgical technique for reconstructing the scapular and proximal humeral regions following sarcoma resection. CASE REPORT We present 2 cases involving patients diagnosed with dedifferentiated chondrosarcoma and fibrosarcoma in the scapulohumeral region who underwent radical tumor excision followed by a novel joint reconstruction technique. In each case, a humeral diaphyseal hip spacer with dual antibiotic-loaded cement was used. A new joint capsule was constructed with Trevira mesh, affixed to both the clavicle and the second costal arch to anchor the remaining structures. Functional outcomes were evaluated using the Musculoskeletal Tumor Society (MSTS) score and the Toronto Extremity Salvage Score (TESS). Both patients achieved favorable clinical outcomes, with disease-free resection margins, satisfactory aesthetic outcomes, and acceptable postoperative shoulder contour and function. CONCLUSIONS Joint reconstruction using a cemented humeral diaphyseal hip spacer anchored to the clavicle with Trevira mesh restores structural integrity and partially recovers glenohumeral function. This technique also addresses aesthetic deficits associated with extensive scapulohumeral tumor resection, presenting a promising alternative for functional and cosmetic rehabilitation due to the structural offset provided by the hip spacer. However, larger studies are necessary to validate these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".