Anticyclic Citrullinated Peptide Antibodies in Patients with Rheumatic Diseases other than Rheumatoid Arthritis: Clinical or Pathogenic Significance?
Bibliographic record
Abstract
To the Editor: We read with interest about the study by Payet, et al investigating prevalence and discriminatory value of anticyclic citrullinated peptide (anti-CCP) antibodies in patients with rheumatic diseases other than rheumatoid arthritis (RA)1. Although several studies have been published aiming to identify the prevalence of anti-CCP in patients with different rheumatic conditions2,3,4, Payet’s study was performed in a very large patient cohort and represents, therefore, a relevant information source on this topic. The overall prevalence of anti-CCP in the 723 non-RA patients was rather high compared to other studies5,6,7, with a very high proportion of patients with connective tissue disease (CTD) with anti-CCP (17.5%). This allowed the conclusion that, although anti-CCP are rather specific for RA, they are consistently expressed in patients with other rheumatic diseases. However, it should be noted that, unlike previously published studies analyzing anti-CCP prevalence in non-RA disorders, the design of Payet’s investigation considered the analysis of … Address correspondence to Prof. R. Gerli, Rheumatology Unit, Department of Medicine, University of Perugia, Via Enrico dal Pozzo snc, I-06122, Perugia, Italy. E-mail: roberto.gerli{at}unipg.it
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".