Serologic phenotypes distinguish systemic lupus erythematosus patients developing interstitial lung disease and/or myositis
Bibliographic record
Abstract
OBJECTIVES: To determine if serologic phenotypes could be identified in systemic lupus erythematosus patients developing interstitial lung disease (ILD) and/or myositis. METHODS: Adult SLE patients (without myositis/ILD at baseline) had annual assessments and serum sampling between 2000 and 2017. New-onset ILD was identified using the SDI pulmonary fibrosis item. New-onset myositis was identified using the SLICC Damage Index muscle atrophy/weakness item, the SLEDAI-2K item for myositis, and annual creatinine kinase testing. Chart review confirmed ILD/myositis cases and randomly sampled SLE patients from baseline formed our sub-cohort (N = 72). Cases and sub-cohort were compared regarding myositis-related biomarkers at baseline and at a randomly selected follow-up between baseline and end of observation (date of ILD/myositis diagnosis or Dec. 31, 2017). Descriptive analyses and hazards ratios (HRs) were generated for ILD/myositis incidence, focusing on baseline serology and adjusting for sex, race/ethnicity, age at SLE diagnosis, and SLE duration. RESULTS: Fourteen SLE patients developed ILD (N = 9), myositis (N = 3), and/or both (N = 2). Thirteen of those (92.9%) developing ILD/myositis had at least one biomarker at baseline, versus 47 (65.3%) SLE patients who never developed myositis/ILD. The most common biomarkers in myositis/ILD were KL-6, anti-Ro52, and anti-Ku. Baseline biomarkers tended to remain positive in follow-up. In multivariate Cox regressions, SLE patients had higher risk of developing myositis/ILD with elevated baseline KL-6 (adjusted hazard ratio 3.66; 95% confidence interval 1.01, 13.3). When updating biomarkers over time, we also saw correlations between anti-Smith and ILD/myositis. CONCLUSIONS: Baseline myositis-related biomarkers were highly associated with ILD/myositis incidence. This is the first identification of biomarker phenotypes with ILD/myositis risk in SLE.
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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 | 0.001 | 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".