Charlson Comorbidity Index Is Related to Organ Damage in Systemic Lupus Erythematosus: Data from KORean lupus Network (KORNET) Registry
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify whether comorbidity status is associated with organ damage in patients with systemic lupus erythematosus (SLE). METHODS: A total of 502 patients with SLE enrolled in the KORean lupus Network were consecutively recruited. Data included demographics, age-adjusted Charlson Comorbidity Index (CCIa), disease activity indexes, the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI), the Medical Outcomes Study Short Form-36 health survey (SF-36) score, and the Beck Depression Inventory (BDI) score. RESULTS: Of the total patients, 21.1% (n = 106) experienced organ damage (SDI ≥ 1). Univariate correlation analysis revealed that SDI was not statistically correlated with any clinical variables (correlation coefficient r < 0.3 of all). There were significant differences in the BDI, mental component score of the SF-36, Systemic Lupus Erythematosus Disease Activity Index (SLEDAI), CCIa, C-reactive protein, and mean dose of corticosteroid between non-damage (SDI = 0) and damage (SDI ≥ 1) groups. The presence of damage to at least 1 organ in patients with SLE was found to be closely related with higher CCIa, higher SLEDAI, and mean dose of corticosteroid (OR 1.884, 95% CI 1.372-2.586, p < 0.001; OR 1.114, 95% CI 1.041-1.192, p = 0.002; OR 1.036, 95% CI 1.004-1.068, p = 0.026; respectively) in binary logistic regression analysis. CONCLUSION: This study suggests that organ damage as assessed by the SDI in Korean patients with SLE is related to comorbidities together with disease activity and corticosteroid exposure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".