The Use of Neo – Adjuvant Denosumab in Treatment of Giant Cell Tumours of the Spine
Bibliographic record
Abstract
Abstract Background The current recommended treatment for Giant Cell Tumour of the spine is en bloc excision. Denosumab is a monoclonal RANKL inhibitor that shows promising results when used as a neo – adjuvant treatment. The purpose of this study was to assess the effect of Denosumab on tumour characteristics and symptom relief. Methods We performed a retrospective review of 12 patients treated with denosumab as neo adjuvant and stand - alone treatment. Tumour volume and PET SUV capitation measurements were taken before and after treatment and patients were interviewed for subjective pain responses. Clinical response was determined by reduction in tumour volume, PET SUV capitation, the Boriani calcification response classification, improvement in the Bilsky epidural grading and WBB layers and improvement in pain. Results Following treatment 75% of patients were pain free with 58% noting improvement within 48 hours. Mean relative volumetric reduction in tumour volume was 42%. All pathology specimens confirmed elimination of giant cells. Improvement in Bilsky epidural disease grading occurred in 7/12 cases. Median baseline SUVmax was 14.7 and post treatment was 3.3. Sixty - seven percent of patients demonstrated intralesional bone formation following treatment. At one year follow-up, there were no cases of local disease recurrence, malignant transformation or metastases. Conclusions This study demonstrates neo-adjuvant denosumab can facilitate en bloc resection by reducing the tumour burden around critical adjacent neurovascular structures, reducing the risk morbidity and improving preoperative pain. We recommend routine use when W-B-B – based criteria are fulfilled for en – bloc excision.
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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 | 0.001 |
| 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) | 0.000 | 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".