The role of Denosumab in joint preservation for patients with giant cell tumour of bone
Bibliographic record
Abstract
AIMS: Local recurrence remains a challenging and common problem following curettage and joint-sparing surgery for giant cell tumour of bone (GCTB). We previously reported a 15% local recurrence rate at a median follow-up of 30 months in 20 patients with high-risk GCTB treated with neoadjuvant Denosumab. The aim of this study was to determine if this initial favourable outcome following the use of Denosumab was maintained with longer follow-up. METHODS: Patients with GCTB of the limb considered high-risk for unsuccessful joint salvage, due to minimal periarticular and subchondral bone, large soft tissue mass, or pathological fracture, were treated with Denosumab followed by extended intralesional curettage with the goal of preserving the joint surface. Patients were followed for local recurrence, metastasis, and secondary sarcoma. RESULTS: A total of 25 patients with a mean age of 33.8 years (18 to 67) with high-risk GCTB received median six cycles of Denosumab before surgery. Tumours occurred most commonly around the knee (17/25, 68%). The median follow-up was 57 months (interquartile range (IQR) 13 to 88). The joint was salvaged in 23 patients (92%). Two required knee arthroplasty due to intra-articular fracture and arthritis. Local recurrence developed in 11 patients (44%) at a mean of 32.5 months (3 to 75) following surgery, of whom four underwent repeat curettage and joint salvage. One patient developed secondary osteosarcoma and another benign GCT lung metastases. CONCLUSION: 2021;103-B(1):184-191.
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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 | 0.000 | 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) | 0.001 | 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".