Long-term Results for Limb Salvage with Osteoarticular Allograft Reconstruction
Bibliographic record
Abstract
UNLABELLED: Osteoarticular allograft reconstruction after extremity tumor resection has been shown to have a high rate of complications. Although good functional results have been seen, long-term outcomes have not been well studied. We performed a retrospective review of 20 patients who underwent primary osteoarticular allograft reconstruction after extremity sarcoma resection. All postoperative complications related to the allograft reconstruction were recorded. Musculoskeletal Tumor Society 1993 and Toronto Extremity Salvage Score scores were used for functional evaluation at last followup. Minimum followup was 10 years (mean, 16 years; range, 10-21 years). Seventy percent of patients experienced an event during the followup period. Recorded events were fracture (nine patients), progressive arthritis (five), nonunion (four), and infection (two). Sixty percent of allografts were removed at a mean of 5.2 years. Progressive arthritis led to total joint arthroplasty in five patients (25%). Mean Musculoskeletal Tumor Society and Toronto Extremity Salvage Score functional scores were 25 of 30 and 95% for patients who retained their original allograft. Osteoarticular allograft reconstruction for extremity sarcomas had a high rate of adverse events (70%) and allograft removal (60%) at long-term followup. Functional outcomes of patients with intact grafts were comparable to outcomes with segmental replacement prostheses reported in the literature. LEVEL OF EVIDENCE: Level IV, therapeutic study. See the Guidelines for Authors for a complete description of levels of evidence.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".