Outcomes in Systemic Sclerosis–Associated Interstitial Lung Disease Based on Serological Profiles With a Focus on Anticentromere and Anti-RNA Polymerase III Antibodies
Bibliographic record
Abstract
OBJECTIVE: We aimed to compare the progression of systemic sclerosis-associated interstitial lung disease (SSc-ILD) based on serological status. METHODS: In a posthoc analysis of the SENSCIS trial (nintedanib vs placebo in SSc-ILD; ClinicalTrials.gov: NCT02597933), we analyzed the rate of decline in forced vital capacity (FVC) over 52 weeks in 3 subsets: (1) positive for anticentromere antibody (ACA), (2) positive for anti-RNA polymerase III antibody (ARA), and (3) negative for ACA, ARA, and antitopoisomerase I antibody (ATA). RESULTS: Among study participants who underwent baseline serological evaluation, 32/549 (5.8%) were ACA positive, 98/528 (18.6%) were ARA positive, and 127/526 (24.1%) were negative for ACA, ARA, and ATA. Among the serological subsets of interest, in the placebo arm, the adjusted rate (standard error) of decline in FVC was -31.2 (41.5) mL/year among participants who were positive for ACA and -64.7 (35.1) mL/year among participants who were positive for ARA, numerically lower than in the overall SENSCIS trial population (-93.3 [13.5] mL/yr). However, participants who were negative for ACA, ARA, and ATA experienced a numerically greater rate of decline in FVC than the overall trial population, both in those randomized to placebo (-115.6 [35.4] mL/yr vs -93.3 [13.5] mL/yr) and those randomized to nintedanib (-91.8 [34.3] mL/yr vs -52.4 [13.8] mL/yr). CONCLUSION: These analyses of data from the SENSCIS trial suggest that patients with SSc-ILD who are ACA positive or ARA positive can experience progression of SSc-ILD. Patients negative for ACA, ARA, and ATA had a higher rate of progression than the overall trial population and should be monitored closely.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".