Outcome of Reoperation for Local Recurrence Following En Bloc Resection for Bone Giant Cell Tumor of the Extremity
Bibliographic record
Abstract
En bloc resection is typically performed to treat giant cell tumors of bone (GCTB), particularly when curettage can be challenging owing to extensive bone cortex destruction with soft tissue extension. Few reports have addressed the clinical outcomes after reoperation for local recurrence in patients with GCTB who underwent en bloc resection. In this multicenter retrospective study, we investigated local recurrence, distant metastasis, malignant transformation, mortality, and limb function in patients treated for local recurrence following en bloc resection for GCTB. Among 205 patients who underwent en bloc resection for GCTB of the extremities between 1980 and 2021, we included 29 with local recurrence. En bloc resection was performed for large tumors with soft tissue extension, pathological fractures with joint invasion, complex fractures, and dispensable bones, such as the proximal fibula and distal ulna. Local re-recurrence, distant metastasis, malignant transformation, and mortality rates were 41.4% (12/29), 34.5% (10/29), 6.9% (2/29), and 6.9% (2/29), respectively. The median Musculoskeletal Tumor Society score was 26 (interquartile range, 23-28). The median follow-up period after surgery for local recurrence was 70.1 months (interquartile range, 40.5-123.8 months). Local recurrence following en bloc resection for GCTB could indicate an aggressive GCTB, necessitating careful follow-up.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".