Development of a molecular test of Paget's disease of bone
Bibliographic record
Abstract
Depending on populations, 15 to 40% of patients have a familial form of Paget's disease of bone (PDB), which is transmitted in an autosomal-dominant mode of inheritance with incomplete penetrance. To date, only SQSTM1 gene mutations have been linked to the disease. Several single nucleotide polymorphisms (SNPs) have been associated with PDB in patient non-carriers of SQSTM1 mutations, but they have minor size effects. The current clinical practice guidelines still recommend to measure total serum alkaline phosphatase (sALP) for PDB screening. However, genetic or bone biomarkers alone may lack sensitivity to detect PDB. Thus, the objective of this study was to develop a molecular test of PDB, combining genetic and bone biomarkers, in order to detect PDB, which is frequently asymptomatic. We genotyped 35 SNPs previously associated with PDB in 305 patients, and 292 healthy controls. In addition, serum levels of 14 bone biomarkers were assayed in 51 patients and 151 healthy controls. Bivariate and multivariate logistic regression models with adjustment for age and sex were fitted to search for a combination of SNPs and/or bone biomarkers that could best detect PDB in patient non-carriers of SQSTM1 mutations. First, a combination of five genetic markers gave rise to the highest area under the ROC curve (AUC) with 95% confidence interval [95% CI] of 0.731 [0.688; 0.773], which allowed us to detect 81.5% of patients with PDB. Second, a combination of two bone biomarkers had an AUC of 0.822 [0.726; 0.918], and was present in 81.5% of patients with PDB. Then, the combination of the five genetic markers and the two bone biomarkers increased the AUC up to 0.892 [0.833; 0.951], and detected 88.5% of patients with PDB. These results suggested that an algorithm integrating first a screen for SQSTM1 gene mutations, followed by either a genetic markers combination or a combined genetic and biochemical markers test in patients non-carrier of any SQSTM1 mutation, may detect the PDB phenotype better than biomarkers already available in the clinical practice.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".