Experience with denosumab (XGEVA®) for prevention of skeletal-related events in the 10 years after approval
Bibliographic record
Abstract
Skeletal-related events (SREs) are complications of bone metastases and carry a significant patient and economic burden. Denosumab is a receptor activator of nuclear factor-κB ligand (RANKL) inhibitor approved for SRE prevention in patients with multiple myeloma and patients with bone metastases from solid tumors. In phase 3 trials, denosumab showed superiority to the bisphosphonate zoledronate in reducing the risk of first on-study SRE by 17% (median time to first on-study SRE delayed by 8.2 months) and the risk of first and subsequent on-study SREs by 18% across multiple solid tumor types, including some patients with multiple myeloma. Denosumab also improved pain outcomes and reduced the need for strong opioids. Additionally, a phase 3 trial showed denosumab was noninferior to zoledronate in delaying time to first SRE in patients with newly diagnosed multiple myeloma. Denosumab has a convenient 120 mg every 4 weeks recommended dosing schedule with subcutaneous administration. Rare but serious toxicities associated with denosumab include osteonecrosis of the jaw, hypocalcemia, and atypical femoral fracture events, with multiple vertebral fractures reported following treatment discontinuation. After a decade of real-world clinical experience with denosumab, we are still learning about the optimal use and dosing for denosumab. Despite the emergence of novel and effective antitumor therapies, there remains a strong rationale for the clinical utility of antiresorptive therapy for SRE prevention. Ongoing studies aim to optimize clinical management of patients using denosumab for SRE prevention while maintaining safety and efficacy.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".