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Record W4322625757 · doi:10.1016/j.bonr.2023.101670

Molecular test of Paget's disease of bone in families not linked to SQSTM1 gene mutations

2023· article· en· W4322625757 on OpenAlexafffund
Yang You, David Simonyan, Alexandre Bureau, Édith Gagnon, Caroline Albert, Jason R. Guertin, Jean‐Éric Tarride, Jacques P. Brown, Laëtitia Michou

Bibliographic record

VenueBone Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsMcMaster UniversityImpactCentre Hospitalier de l’Université de MontréalCentres Intégré Universitaires de Santé et de Services SociauxUniversité Laval
FundersCanadian Institutes of Health ResearchServierFondation CHU de QuébecNovo NordiskCanada Foundation for InnovationEli Lilly and CompanyCentre Hospitalier Universitaire de QuébecGilead SciencesRadius HealthUltragenyx PharmaceuticalFonds de Recherche du Québec - SantéUniversité LavalAmgenEdwards LifesciencesMereo BioPharmaPfizer
KeywordsProtein Data Bank (RCSB PDB)Paget's disease of boneSingle-nucleotide polymorphismGeneticsGeneMutationBiologyMedicineInternal medicineDiseaseGenotypeBiochemistry

Abstract

fetched live from OpenAlex

Paget's disease of bone (PDB) is a focal metabolic bone disorder characterized by an increased bone remodeling. Fifteen to 40 % of PDB patients have a familial form with an autosomal dominant inheritance. Disease-causing mutations of the SQSTM1 gene have been linked to PDB in about 40 % of families whereas genes linked to the remaining families are unknown. Several single nucleotide polymorphisms (SNPs) have been associated with PDB in unrelated patient non-carriers of a SQSTM1 mutation. The current clinical practice guidelines still recommend the measure of serum total alkaline phosphatase (sALP) for PDB screening. In unrelated individual non-carriers of SQSTM1 mutations, we previously developed a genetic test combining male sex with five genetic markers (rs499345, rs5742915, rs2458413, rs3018362, rs2234968), giving rise to an area under the curve (AUC) for PDB phenotype of 0.73 (0.69; 0.77). A combination of male sex with total calcium corrected for albumin and Procollagen type I N-terminal propeptide (P1NP), had an AUC of 0.82 (0.73; 0.92). Combining both genetic and biochemical tests increased the AUC to 0.89 (0.83; 0.95). This study aimed at estimating the performance of our previous test of PDB, in families not linked to SQSTM1 mutations with disease-causing genes yet unknown, and at developing a new algorithm if the performance is not satisfactory. We genotyped the five SNPs cited above, and measured calcium corrected for albumin and P1NP in 181 relatives, with PDB or not, from 19 PDB families not linked to SQSTM1 mutations. Bivariate and multivariate logistic regression models including male sex were fitted to search for a molecular test that could best detect PDB in these families. A receiving operating characteristics analysis was done to establish a cut-off point for continuous variables. Logistic regression estimates of our previous molecular test gave rise to a high sensitivity of 78 %, 97 % and 88 % for the genetic, biochemical, and combined test but the specificity was very low, 35 %, 11 % and 21 %, respectively. This poor specificity persisted even when the cut-off point was changed. We then generated in these families, new logistic regression estimates but on the same parameters as mentioned above, giving rise to an AUC of 0.65 (0.55; 0.75) for the genetic test, of 0.84 (0.74; 0.94) for the biochemical test, and 0.89 (0.82; 0.96) for the combination test, the latter having a sensitivity of 96 % and specificity of 57 %. By comparison serum P1NP alone gave rise to an AUC of 0.84 (0.73; 0.94), with a sensitivity of 71 % and a specificity of 79 %. In PDB families not linked to SQSTM1 mutations, the estimates of our previous molecular test gave rise to a poor specificity. Using new estimates, the biochemical and combined tests have similar predictive abilities than our former test. Serum P1NP is a bone marker of interest for the screening for PDB in families not linked to SQSTM1 mutations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.309
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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