Analysis of Environmental Factors in Familial Versus Sporadic Paget's Disease of Bone—The New England Registry for Paget's Disease of Bone
Bibliographic record
Abstract
UNLABELLED: A registry for Paget's Disease (PD) was created to study the environmental and familial features of this disorder. This study examines the first 202 people enrolled. Those with a family history of PD (20%) suffered earlier deformity and fracture, and tended to have grandparents born abroad. These findings suggest heritage is important in PD; the role of environment remains unclear. INTRODUCTION: The New England Registry for Paget's Disease of Bone is a database that was created to explore the distribution and determinants of disease frequency. METHODS: Using a case-series design, we explored the association of environmental factors in expression of the disease, comparing those patients with familial Paget's disease to those with sporadic Paget's disease (PD). RESULTS AND CONCLUSIONS: Analysis of the first 202 patients enrolled in the registry revealed a positive family history (FH) in 41 (20%). Significant findings in this cohort included an earlier age of onset (51 years FH+ versus 59 years FH-, p < 0.05), a trend to a higher incidence of bone deformity (49% versus 33%, p < 0.1), and an increased fracture rate (27% versus 11%, p < 0.05). Persons with a FH of PD were less likely to record the United States or Canada as their grandparents' birthplace (p < 0.01), and sibships tended to be larger (p < 0.05). A history of measles infection, childhood exposure to pets, milk ingestion, year of immigration to the United States, birth order, level of education, and functional status did not distinguish the two groups. The database confirms the high prevalence of familial PD and supports the theory that heritable factors are important in the pathogenesis of this focal disorder of bone metabolism.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".