Severe gastrointestinal disease in very early systemic sclerosis is associated with early mortality
Bibliographic record
Abstract
OBJECTIVES: To examine the incidence, predictors and outcomes associated with severe gastrointestinal (GI) disease in a large inception SSc cohort. METHODS: SSc subjects with <2 years of disease duration were identified from two multicentre cohorts. Severe GI disease was defined as: malabsorption, hyperalimentation, pseudo-obstruction and/or ⩾10% weight loss in association with the use of antibiotics for bacterial overgrowth or oesophageal stricture. Kaplan-Meier, multivariate logistic regression and Cox proportional hazard analyses were performed to determine the cumulative incidence rate, independent clinical correlates and mortality rate associated with severe GI disease. A longitudinal mixed model was used to assess the impact of severe GI disease on the Short Form Health Survey. RESULTS: In this inception SSc cohort, the probability of developing severe GI disease was estimated at 9.1% at 2 years and 16.0% at 4 years. In multivariate analysis, severe GI disease was associated with inflammatory myositis (odds ratio 4.68, 95% CI 1.65, 13.24), telangiectasias (odds ratio 2.45, 95% CI 1.19, 5.04) and modified Rodnan skin score (odds ratio 1.03, 95% CI 1.01, 1.07). Severe GI disease was associated with a >2-fold increase in the risk of death (hazard ratio 2.27, 95% CI 1.27, 4.09) and worse health-related quality of life [Short Form Health Survey physical (β = -2.37, P = 0.02) and mental (β = -2.86, P = 0.01) component summary scores]. CONCLUSION: Severe GI disease is common in early SSc and is associated with significant morbidity and increased mortality. More research is needed to understand, prevent and mitigate severe GI disease in SSc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".