Advances in the genetics of Paget’s disease of bone: from pathophysiology, diagnosis to clinical implications
Bibliographic record
Abstract
Introduction Paget’s Disease of Bone (PDB) is a chronic, late-onset skeletal disorder characterized by highly localized regions of increased bone resorption, accompanied by excessive and abnormal new bone formation. Consequently, PDB serves as a crucial model for elucidating the genetic and molecular mechanisms governing both abnormal osteoclast formation and osteoclast-induced osteoblast/osteocyte activities.Areas covered This narrative review based on the available indexed literature examines the genetics of PDB and its connections with the pathophysiology of the disease, focusing on interactions with bone cells, environmental factors, clinical presentation, and complications.Expert opinion PDB research highlights the importance of genetic studies, and the need for further research especially in families not linked to SQSTM1 pathogenic variants. Enhancing education for health care professionals is critical, given PDB’s rarity and its exclusion from medical curricula, which leads to diagnostic delays and mismanagement. Further exploration of gene-environmental interactions and the role of bone cells in lesion formation remains a priority. Advances in precision medicine may soon allow genetic testing to predict the risk of PDB using polygenic risk scores, leading to targeted prevention strategies. Future treatments may involve SQSTM1 gene inhibitors, such as siRNA, offering a personalized approach to early prevention and management of PDB.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".