Demographic and Clinical Features Related to a Symptomatic Onset of Paget’s Disease of Bone
Bibliographic record
Abstract
OBJECTIVE: Paget's disease of bone (PDB) is a focal disorder of skeletal remodeling that can lead to bone pain, deformity, and fractures, but it can often be asymptomatic for a long time. This study investigated which factors may distinguish patients with clinical manifestations from asymptomatic patients. METHODS: The study group consisted of 224 patients with PDB referred to our Bone Disease Unit. For all patients, data were collected about clinical and demographic variables and diagnostic procedures. Logistic regression analyses were used to assess the role of recorded variables on the odds of being diagnosed clinically rather than by chance. RESULTS: Among the 124 patients with clinical manifestations leading to the diagnosis (55.4%), 36 subjects complained of bone pain, 32 articular pain, 42 back pain, 2 headache; 9 had fractures in Paget bone, and 3 had bone deformity. In 100 patients (44.6%) PDB was diagnosed by chance. At the multivariate analysis, only the number of bones involved (OR for 1 site increment = 1.18, 95% CI: 1.007-1.402; p = 0.04) acted as an independent predictor for a clinical diagnosis. Some skeletal localizations were associated with a clinical diagnosis: the involvement of lumbar spine (OR = 2.085, 95% CI: 1.024-4.224; p = 0.043) was more likely in symptomatic patients; pelvis and tibia showed a borderline statistical significance. The skull was predictive for asymptomatic PDB. CONCLUSION: A systematic laboratory screening including serum alkaline phosphatase of an older subject complaining of bone pain, articular pain, or back pain is the sole strategy to improve the diagnostic sensitivity for 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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".