Outcomes of osteoarticular ulna allograft for the reconstruction of proximal ulna tumour
Bibliographic record
Abstract
Aims: The ulna is an extremely rare location for primary bone tumours of the elbow in paediatrics. Although several reconstruction options are available, the optimal reconstruction method is still unknown due to the rarity of proximal ulna tumours. In this study, we report the outcomes of osteoarticular ulna allograft for the reconstruction of proximal ulna tumours. Methods: Medical profiles of 13 patients, who between March 2004 and November 2021 underwent osteoarticular ulna allograft reconstruction after the resection of the proximal ulna tumour, were retrospectively reviewed. The outcomes were measured clinically by the assessment of elbow range of motion (ROM), stability, and function, and radiologically by the assessment of allograft-host junction union, recurrence, and joint degeneration. The elbow function was assessed objectively by the Musculoskeletal Tumor Society (MSTS) score and subjectively by the Toronto Extremity Salvage Score (TESS) and Mayo Elbow Performance Score (MEPS) questionnaire. Results: The mean follow-up of patients was 60.3 months (SD 28.5). The mean elbow flexion-extension ROM was 95.8° (SD 21). The mean MSTS of the patients was 84.4 (SD 8.2), the mean TESS was 83.8 (SD 6.7), and the mean MEPS was 79.2 (SD 11.5). All the patients had radiological union at the osteotomy site. Symptomatic osteoarthritic change was observed in three patients (23%), one of whom ended up with elbow joint fusion. Two patients (15.4%) had recurrence during the follow-up period. Surgical complications included two allograft fractures, two plate fractures, three medial instabilities, and two infections. Conclusion: Osteoarticular ulna allograft reconstruction provides acceptable functional outcomes. Despite a high rate of complications, it is still a valuable reconstruction method, particularly in skeletally immature patients who need their distal humerus physis for the rest of hand growth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".