Soluble Biomarkers May Differentiate Psoriasis from Psoriatic Arthritis
Bibliographic record
Abstract
Patients with psoriatic arthritis (PsA) have a higher inflammatory burden and poorer quality of life compared to patients with psoriasis without PsA. Early identification of PsA may prevent joint damage progression and improve quality of life. Soluble biomarkers have the potential to be useful for screening patients with psoriasis for underlying PsA so that appropriate referral to a rheumatologist is made. Pilot studies have shown that C-reactive protein, interleukin 6, cartilage oligomeric matrix protein (COMP), Dickkopf-1, and macrophage-colony stimulating factor may differentiate PsA from psoriasis without PsA. Compared with controls, increased serum levels of receptor activator of nuclear factor-κB ligand, tumor necrosis factor superfamily member 14, matrix metalloproteinase-3 (MMP-3), and COMP are independently associated with psoriatic disease. Increased levels of high-sensitivity CRP (hsCRP), osteoprotegerin (OPG), MMP-3, and the ratio of C-propeptide of type II collagen (CPII) to collagen fragment neoepitopes Col2-3/4 C(long mono) (C2C) are independently associated with PsA. A combination of hsCRP, OPG, MMP-3, and the ratio CPII of C2C was able to distinguish patients with PsA from those with psoriasis alone in a receiver-operating characteristic curve analysis, with area under the curve 0.904. Therefore, a combination of the above biomarkers may at least have a role in screening patients with psoriasis for PsA. These findings need to be validated in prospective studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".