Serum Anti-Aminoacyl-Transfer Ribonucleic Acid Synthetase Antibody Levels Are Involved in Rheumatoid Arthritis Complicated with Interstitial Lung Disease
Bibliographic record
Abstract
Objectives: A common complication in patients with rheumatoid arthritis (RA) is interstitial lung disease (ILD). Antibodies (Abs) to anti-aminoacyl-transfer ribonucleic acid synthetase (ARS) are linked to ILD in patients with idiopathic inflammatory myopathies (IIM). There have been limited studies of anti-ARS Abs in RA. In this study, we examined anti-ARS Abs in ILD in patients with RA. Methods: Anti-ARS Abs in serum from patients with RA were measured. Results: There were higher anti-ARS Ab levels in RA patients with ILD (mean ± SDM, 16.3 ± 32.3 vs. 7.4 ± 7.0 (Index), p = 5.58 × 10−12), usual interstitial pneumonia (14.4 ± 24.4 vs. 7.4 ± 7.0 [Index], p = 3.14 × 10−12), and nonspecific interstitial pneumonia (17.9 ± 37.7 vs. 7.4 ± 7.0 (Index), p = 5.07 × 10−5) compared with patients without chronic lung disease. The area under the curve (AUC) of the receiver operating characteristic curve for anti-ARS Ab was too low to allow for discrimination among RA patients with/without chronic lung disease (0.608, 95% confidence interval (CI) 0.560–0.655, p = 8.69 × 10−6). Multiple logistic regression analyses of age, smoking status, anti-ARS Abs, as well as Steinbrocker stage generated an ARS-index with a high AUC value (0.707, 95%CI 0.662–0.752, p = 2.20 × 10−19). Conclusions: Anti-ARS Abs are related to ILD pathogenesis in RA and may be a biomarker for ILD.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".