Surgical Downstaging in an Open-Label Phase II Trial of Denosumab in Patients with Giant Cell Tumor of Bone
Bibliographic record
Abstract
BACKGROUND: Surgical resection with curative intent for giant cell tumor of bone (GCTB) may be associated with severe morbidity. This interim analysis evaluated reduction in surgical invasiveness after denosumab treatment in patients with resectable GCTB. METHODS: Patients with primary or recurrent GCTB, for whom the initially planned surgery was associated with functional compromise or morbidity, received denosumab 120 mg subcutaneously every 4 weeks (additional doses on days 8 and 15 of the first cycle). Planned and actual GCTB-related surgical procedures before and after denosumab treatment were reported. Patients were followed for surgical outcome, adverse events, and recurrence following resection. RESULTS: Overall, 222 patients were evaluable for surgical downstaging (54 % were women; median age 34 years). Lesions (67 % primary and 33 % recurrent) were located in the axial (15 %) and appendicular skeleton (85 %). At the data cutoff date, most patients had not yet undergone surgery (n = 106; 48 %) or had a less morbid procedure (n = 84; 38 %) than originally planned. Median (interquartile range) time on denosumab was 19.5 (12.4-28.6) months for the 106 patients who had not undergone surgery and were continuing on monthly denosumab. Native joint preservation was 96 % (n = 24/25) for patients with planned joint/prosthesis replacement and 86 % (n = 30/35) for patients with planned joint resection/fusion. Of the 116 patients who had surgery (median postsurgical follow-up 13.0 [8.5-17.9] months), local recurrence occurred in 17 (15 %) patients. CONCLUSION: For patients with resectable GCTB, neoadjuvant denosumab therapy resulted in beneficial surgical downstaging, including either no surgery or a less morbid surgical procedure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".