Risk Factors for Future Scleroderma Renal Crisis at Systemic Sclerosis Diagnosis
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a disease of autoimmunity, fibrosis, and vasculopathy. Scleroderma renal crisis (SRC) is one of the most severe complications. Corticosteroid exposure, presence of anti-RNA polymerase III antibodies (ARA), skin thickness, and significant tendon friction rubs are among the known risk factors at SSc diagnosis for developing future SRC. Identification of additional clinical characteristics and laboratory findings could expand and improve the risk profile for future SRC at SSc diagnosis. METHODS: In this retrospective cohort study of the entire military electronic medical record between 2005 and 2016, we compared the demographics, clinical characteristics, and laboratory results at SSc diagnosis for 31 cases who developed SRC after SSc diagnosis to 322 SSc without SRC disease controls. RESULTS: After adjustment for potential confounding variables, at SSc diagnosis these conditions were all associated with future SRC: proteinuria (p < 0.001; OR 183, 95% CI 19.1-1750), anemia (p = 0.001; OR 9.9, 95% CI 2.7-36.2), hypertension (p < 0.001; OR 13.1, 95% CI 4.7-36.6), chronic kidney disease (p = 0.008; OR 20.7, 95% CI 2.2-190.7), elevated erythrocyte sedimentation rate (p < 0.001; OR 14.3, 95% CI 4.8-43.0), thrombocytopenia (p = 0.03; OR 7.0, 95% CI 1.2-42.7), hypothyroidism (p = 0.01; OR 2.8, 95% CI 1.2-6.7), Anti-Ro antibody seropositivity (p = 0.003; OR 3.9, 95% CI 1.6-9.8), and ARA (p = 0.02; OR 4.1, 95% CI 1.2-13.8). Three or more of these risk factors present at SSc diagnosis was sensitive (77%) and highly specific (97%) for future SRC. No SSc without SRC disease controls had ≥ 4 risk factors. CONCLUSION: In this SSc cohort, we present a panel of risk factors for future SRC. These patients may benefit from close observation of blood pressure, proteinuria, and estimated glomerular filtration rate, for earlier SRC identification and intervention. Future prospective therapeutic studies could focus specifically on this high-risk population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".