Soluble biomarkers differentiate patients with psoriatic arthritis from those with psoriasis without arthritis
Bibliographic record
Abstract
OBJECTIVE: Biomarkers may be helpful in screening patients with psoriasis for PsA. Our purpose was to identify serum biomarkers for psoriasis and PsA. METHODS: Fifty-two patients with psoriasis (26 satisfying CASPAR classification criteria for PsA) and 26 healthy controls were recruited for our study. Patients with psoriasis and PsA were group matched for age, sex and psoriasis duration, whereas controls were matched for age and sex. Blood samples were drawn at the time of assessment and serum was analysed for the following: IL-12, IL-12p40, IL-17, TNF super family member 14 (TNFSF14), MMP-3, RANK ligand (RANKL), osteoprotegerin (OPG), cartilage oligomeric matrix protein (COMP), C-propeptide of Type II collagen (CPII), collagen fragment neoepitopes Col2-3/4(long mono) (C2C) and Col2-3/4(short) (C1-2C) and highly sensitive CRP (hsCRP). Data were analysed using logistic regression and receiver operating characteristic curves were plotted. RESULTS: Fifty-two patients with psoriatic disease had a mean age of 46 years and psoriasis duration of 16.8 years. Compared with controls, increased serum levels of RANKL, TNFSF14, MMP-3 and COMP independently associated with psoriatic disease (P < 0.05). Twenty-six PsA patients (mean swollen and/or tender joint count 16, swollen joint count 5) were then compared with 26 patients who had psoriasis alone. Increased levels of hsCRP, OPG, MMP-3 and the CPII : C2C ratio were independently associated with PsA (P < 0.03). CONCLUSION: This pilot study indicates that hsCRP, OPG, MMP-3 and the CPII : C2C ratio are biomarkers for PsA in patients with psoriasis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".