Predictors of interstitial lung disease in early systemic sclerosis: a prospective longitudinal study of the GENISOS cohort
Bibliographic record
Abstract
INTRODUCTION: The objective of the present study was to examine the association of baseline demographic and clinical characteristics with sequentially obtained measurements of forced vital capacity (FVC), expressed as a percentage of the predicted value, and to identify predictors of the decline rate in FVC over time in the Genetics versus Environment in Scleroderma Outcome Study (GENISOS). METHODS: To date, 266 patients have been enrolled in GENISOS, a prospective, observational cohort of patients with early systemic sclerosis. In addition to pulmonary function tests (PFTs), clinical and laboratory data were obtained from each patient. We analyzed 926 FVC measurements utilizing generalized linear mixed models. The predictive significance of baseline variables for the decline rate in FVC was investigated by the interaction term between the variable and the follow-up time within the first 3 years after enrollment as well as throughout the entire follow-up time. RESULTS: The cohort consisted of 125 white, 54 African American, and 77 Hispanic patients with average disease duration of 2.5 years at enrollment. The mean follow-up time was 3.8 years, ranging up to 11.4 years. A number of baseline variables, including antibody status, African American ethnicity, disease type, baseline PFT values, modified Rodnan Skin Score, fibrosis on chest radiograph, and lung and skin subscores of the Severity Index, were associated with serially measured FVC levels. However, only the presence of anti-topoisomerase I antibodies (ATA) was associated with lower FVC levels (P < 0.001) as well as accelerated decline rate in FVC within the first 3 years of follow-up (P = 0.02). None of the baseline variables predicted the rate of decline in FVC on long-term follow-up. Patients with rapidly progressive ILD, however, were under-represented in the long-term follow-up group because the accelerated rate of decline in FVC was associated with poor survival (P = 0.001). CONCLUSIONS: Presence of ATA was the only baseline variable associated with differential FVC levels, predicting the rate of decline in FVC within the first 3 years of follow-up. The association of faster decline in FVC with poor survival further emphasizes the need for identification of predictive biomarkers by collection of genetic information and serial blood samples in cohort studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".