Denosumab in the Treatment of Osteoporosis: 10 Years Later: A Narrative Review
Bibliographic record
Abstract
The fully human monoclonal antibody denosumab was approved for treatment of osteoporosis in 2010 on the basis of its potent antiresorptive activity, which produces clinically meaningful increases in bone mineral density (BMD) and reduces fracture risk at key skeletal sites. At that time, questions remained regarding the long-term safety and efficacy of this receptor activator of nuclear factor kappa-B ligand (RANKL) inhibitor; and with clinical experience, new questions have arisen regarding its optimal use. Here, we examine these questions through the lens of data from the FREEDOM trial program and other studies to determine where denosumab fits in the osteoporosis treatment landscape. Clinical consensus and evidentiary support have grown for denosumab as a highly effective anti-osteoporosis therapy for patients at high risk of fracture. In the 10-year FREEDOM Extension study, denosumab treatment produced progressive incremental increases in BMD, sustained low rates of vertebral fracture, and further reduction in nonvertebral fracture risk without increased risk of infection, cancer, or immunogenicity. There was no evidence that suppression of bone turnover or mineralization was excessive, and rates of osteonecrosis of the jaw (ONJ) and atypical femoral fracture (AFF) were very low. It is now recognized, however, that transitioning to another anti-osteoporosis therapy after denosumab discontinuation is essential to mitigate a transient rebound of bone turnover causing rapid BMD loss and increased risk of multiple vertebral fractures (MVFs). Taken together, the available data show that denosumab has a favorable benefit/risk profile and is a versatile agent for preventing osteoporotic fractures in the short and long term. Video abstract: Denosumab in the Treatment of Osteoporosis-10 Years Later (MP4 62727 KB).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".