A Potential Role for Nivolumab in the Treatment of Fibrous Dysplasia-Related Pain
Bibliographic record
Abstract
Fibrous dysplasia (FD) is a chronic and progressive disorder of bone growth because of decreased osteoblast formation and osteoclast overactivity. Its main symptoms include pain, fracture, and irregular bone growth. Bisphosphonates are the mainstay of therapy for FD with a primary goal of pain relief. A 50-year-old woman presented to ophthalmology in March 2011 with intermittent proptosis, vertical diplopia, and orbital pain. A computed tomography scan of the head revealed a skull base lesion, which was confirmed to be fibrous dysplasia on bone biopsy. Because of significant headache, she was treated with IV pamidronate monthly starting November 2011, which led to pain reduction. Repeated attempts to decrease the frequency of pamidronate were unsuccessful because of breakthrough pain. Oral alendronate and risedronate did not control her symptoms. She remained on risedronate however because of its convenience. In August 2021, she was diagnosed with metastatic melanoma and started nivolumab. Her headache completely resolved for the first time in 10 years. Although nivolumab, a programmed death-1 blocker, has been used in the treatment of bone malignancy, it has not been previously studied in FD. By suppressing RANK ligand-related osteoclastogenesis, nivolumab decreases cancer-associated bone pain. Our case suggests a potential role for nivolumab in treating FD-associated pain.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".