Paget’s disease of bone: an osteoimmunological disorder?
Bibliographic record
Abstract
Osteoimmunology represents a large area of research resulting from the cross talk between bone and immune systems. Many cytokines and signaling cascades are involved in the field of osteoimmunology, originating from various cell types. The RANK/receptor activator of nuclear factor Kappa-B ligand (RANKL)/osteoprotegerin (OPG) signaling has a pivotal role in osteoimmunology, in addition to proinflammatory cytokines such as tumor necrosis factor-α, interleukin (IL)-1, IL-6, and IL-17. Clinically, osteoimmunological disorders, such as rheumatoid arthritis, osteoporosis, and periodontitis, should be classified according to their pattern of osteoimmunological serum biomarkers. Paget's disease of bone is a common metabolic bone disorder, resulting from an excessively increased bone resorption coupled with aberrant bone formation. With the exception of the cellular responses to measles virus nucleocapsid protein and the interferon-gamma signature, the exact role of the immune system in Paget's disease of bone is not well understood. The cytokine profiles, such as the increased levels of IL-6 and the interferon-gamma signature observed in this disease, are also very similar to those observed in other osteoimmunological disorders. As a potential osteoimmunological disorder, the treatment of Paget's disease of bone may also benefit from progress made in targeted therapies, in particular for receptor activator of nuclear factor Kappa-B ligand and IL-6 signaling inhibition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".