Impending radiographic erosive progression over the following year in a cohort of consecutive patients with inflammatory polyarthritis: prediction by serum biomarkers
Bibliographic record
Abstract
BACKGROUND/PURPOSE: To evaluate biomarkers as predictors of impending erosion progression. METHODS: Variables were measured at baseline and annually up to 5 years in patients with recent-onset polyarthritis treated to zero swollen joints. Erosive status was defined as ≥5 Units in Sharp/van der Heijde Erosion Score; Rapid Erosive Progression (REP) was defined as an increase ≥5 Units in Erosion Scores between consecutive visits. Generalised estimating equations (GEEs) evaluated the effect on REP of positive anticyclic citrullinated peptides (ACPAs) and/or rheumatoid factor (RF), C-reactive protein ˃8.0 mg/L (High-CRP) and 14-3-3η protein ≥0.50 ng/mL (High-14-3-3η), alone and in combinations. RESULTS: Out of 2155 evaluations in 749 consecutive patients, REP occurred after 186 (8.6%) visits, including 13 (2.2%) in patients recruited since 2010. Only 18/537 (3.4%; 6/411 (1.5%) in non-erosive vs 12/126 (9.5%) in patients already erosive) visits without any positive biomarker were followed by REP; at least one biomarker was positive prior to REP in 168/186 (90.3%) visits. Being positive for all four biomarkers conferred a positive predictive value (PPV) of 30.0% (RR 21.8) in patients non-erosive at the visit versus 35.5% (RR 3.07) in those already erosive. High-14-3-3η increased REP only in visits with High-CRP (eg, RR 2.5 to 3.9 when ACPA also positive) and in patients with non-erosive status (eg, RR from 4.3 to 9.4 when also High-CRP). CONCLUSIONS: Adding High-14-3-3η to positive antibodies and CRP improves prediction of impending REP. Although REP is becoming rarer, signatures of biomarkers might help to adapt treatment strategies in at-risk individuals, even those already erosive.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".