The Effect of Denosumab on Pain and Radiological Improvement in Giant Cell Tumours of the Spine in the Acute Setting
Bibliographic record
Abstract
Study Design Retrospective Cohort Study. Objectives The current recommended treatment for Giant Cell Tumour (GCT) of the spine is en bloc excision. Denosumab is a monoclonal antibody reducing osteoclast activity that shows promising results when used as a neo – adjuvant treatment. However, the current literature remains limited. The purpose of this study was to assess the effect of denosumab on tumour characteristics and symptom relief in the acute phase of treatment of spinal GCT. Methods We performed a retrospective review of 16 patients treated with denosumab as neo-adjuvant and stand - alone treatment. MRI and PET tumour characteristics were taken before and after treatment and patients were interviewed for subjective pain responses. Results Following treatment, all patients showed improvement of pain, of which 68.7% of patients were pain free with 43.75% noting improvement within 48 hours. Mean relative volumetric reduction in tumour volume was 37.3% ( P < .001). Eight patients showed high grade of Bilsky classification (Epidural spinal cord compression scale - ESCC) with seven of them showing significant improvement to low grade of ESCC ( P = .016). Median baseline PET Standardised Uptake Value (SUV)max was 14.57 and post treatment was 4.8 ( P < .001). Conclusions This study provides necessary insight to the limited literature on the use of denosumab for spinal GCT in the acute phase. The clinical and radiographic responses observed demonstrate the critical role that neo-adjuvant denosumab has by reducing the tumour burden around critical adjacent neurovascular structures before eventual resection, significant pain improvement even with presence of fractured vertebra.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 teacher head, 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".