Role of Denosumab in the Management of Giant Cell Tumor, a Cross Sectional Study
Bibliographic record
Abstract
ABSTRACT Background: Giant cell tumor (GCT) of bone is a benign lesion which is characterized by presence of multinucleated osteoclasts type giant cells. Nuclear factor kappa B (RANKL) serves as the trigger factor for osteoclasts cells formation. Although surgery is the primary treatment for GCT of bone but recurrence remains the concern. Therefore, the development of denosumab, a monoclonal antibody for treating GCT for both primary and recurrent disease cases. The present study aims to show the role of denosumab along with surgery when given as neo-adjuvant drug to patients of GCT for their treatment. Methodology: A total of 23 patients diagnosed with GCT were included in this study from January 2016–December 2019 and all of these patients had received neo adjuvant denosumab dose of 120 mg SC on day 0, 15, 30 & 45. All patients were treated at the section of Orthopedics, department of surgery, Aga Khan University hospital, Karachi. Other benign lesions were excluded from the study. Results: Out of 23 patients we had 12 (52.2%) males and 11 (47.8%) females. The mean age of our patients was 34±13.8 years and mean follow up duration of all patients was 20.5 ± 10.7 months. There were 15 (65.2%) primary cases of GCT while 8 (34.8%) were recurrent cases. In 8 (34.8%) of the cases primary site of lesion was distal femur followed by 7(30.4%) proximal tibia cases and 3(13%) distal radius cases. In surgical procedure 20 (87%) patients underwent wide margin excision and only 3 (13%) had intralesional curettage. Reconstruction was performed in 21 patients which consist of bone grafting in 9 patients and mega prosthesis insertion in 12 patients. Only two patients had no reconstruction. On final histopathology, there was no residual GCT and we observed no denosumab induced adverse effects. Post-operative complications included wound infection and peri prosthetic infection in 3 patients. On follow-up we had 4(17.4%) cases of recurrence that were offered revision surgery while 19 (82.6%) were disease free. Conclusion: Denosumab has shown successful results in treating patients with GCT along with surgical intervention and it can be a best option for treating recurrent disease as well. Highlights Research registration no: researchregistry4695
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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 | 0.001 | 0.001 |
| 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".