Serum Soluble Bone Turnover Biomarkers in Psoriatic Arthritis and Psoriatic Spondyloarthropathy
Bibliographic record
Abstract
Because psoriatic arthritis (PsA) is an inflammatory disease of joints, serum soluble biomarkers specific for chronic joint and bone inflammation may predict future disease severity and response to therapy, thereby informing stratified medicine approaches. The objectives of our systematic review were to determine whether serum soluble bone and cartilage turnover biomarkers are (1) associated with PsA or psoriatic spondyloarthropathy; and (2) associated with disease activity, disease severity, or clinical phenotype. Ten studies met eligibility criteria. Matrix metalloproteinase (MMP)-3, Dickkopf (DKK)-1, macrophage colony-stimulating factor (M-CSF), crosslinked telopeptide of collagen-1, and tumor necrosis factor-related apoptosis-inducing ligand were associated with PsA, with equivocal results for osteoprotegerin (OPG) and bone alkaline phosphatase (ALP). MMP-3, DKK-1, M-CSF, CPII:C2C (ratio of cartilage degradation vs byproduct formation), and possibly OPG were associated with PsA independently of psoriasis. C1-2C (a neoepitope released when type 2 cartilage is degraded by collagenases) was associated with both tender and swollen joint counts, and bone morphogenetic protein-4 with patient global assessment of disease, pain score, and the Bath Ankylosing Spondylitis Disease Activity Index. Bone ALP was associated with disease activity. M-CSF and receptor activator of nuclear factor-κB ligand were associated with several plain radiographic features. No studies have investigated biomarker associations specifically with axial PsA.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".