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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".