The Role of Denosumab for Surgical Outcomes in Patients with Giant Cell Tumour of Bone: A Systematic Review
Bibliographic record
Abstract
Background: The role of denosumab in patients with resectable giant cell tumour of bone remains unclear. We asked the following research question: for patients (aged ≥ 12 years) with resectable giant cell tumour of bone, what are the benefits and harms of denosumab compared with no denosumab in terms of (1) facilitation of surgery (operative time, blood loss), (2) disease recurrence, (3) pain control, (4) disease stability, and (5) adverse effects (e.g., malignant transformation, osteonecrosis of jaw, atypical femur fracture)? One previous systematic review addressed only one outcome—disease recurrence. Therefore, we undertook this new systematic review to address the above five outcomes. Methods: MEDLINE, EMBASE, PubMed, and Cochrane Database of Systematic Reviews databases were searched on June 30, 2020. Results: This systematic review included one previous systematic review and five comparative studies. Due to poor quality, non-randomized studies fraught with selection bias, it is difficult to determine if a significant difference exists in the outcomes for surgical giant cell tumour of bone with perioperative denosumab. There were no reported cases of adverse effects from denosumab. Conclusion: To date, there is insufficient evidence to understand the value of denosumab in the perioperative setting in patients with giant cell tumour of bone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".