The Acute-phase Response Is Not Predictive for the Development of Arthritis in Seropositive Arthralgia – A Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether markers of the acute-phase response in patients presenting with arthralgia and positive anticitrullinated protein antibodies (ACPA) and/or immunoglobulin M rheumatoid factor (IgM-RF) could be predictive for the development of arthritis. METHODS: In total, 137 ACPA- and/or IgM-RF-positive patients were included. Patients were followed annually for the development of arthritis, defined as presence of 1 or more swollen joints at clinical examination. High-sensitivity C-reactive protein (hsCRP), procalcitonin (PCT), secretory phospholipase A2 (SPLA2), tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), IL-12p70, IL-10, and interferon-γ (IFN-γ) were measured in baseline serum samples. Gene expression focusing on a predefined panel of genes coding for inflammatory molecules was measured by multiplex ligation-dependent probe amplification. RESULTS: Thirty-five patients (26%) developed arthritis within a median time of 11 months (interquartile range 3.7-18 mo). Circulating levels of cytokines, SPLA2, hsCRP, and PCT were not different between patients with progression to clinical arthritis and those without progression. However, a trend for IL-12p70, TNF-α, IL-10, IL-6, and SPLA2 was observed. No correlation between messenger RNA (mRNA) expression levels of inflammatory genes and progression to arthritis was found. Subgroup analysis of patients with early progression to arthritis showed higher levels of mRNA expression of poly(A)-specific ribonuclease and polycomb complex protein BMI-1 compared to patients without progression to arthritis. CONCLUSION: Although low-grade inflammation is present before onset of clinical arthritis in large cohorts and can be detected using consecutive measurements, a single measurement of acute-phase reactants seems to have limited value for prediction of development of arthritis in individual patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".