Predictors of Chronic Kidney Disease in Korean Patients with Lupus Nephritis
Bibliographic record
Abstract
OBJECTIVE: Since chronic kidney disease (CKD) is closely associated with cardiovascular disease and mortality as well as endstage renal disease, prediction of progressive CKD is a clinically important issue. We investigated the independent risk factors for the development of CKD in patients with lupus nephritis (LN). METHODS: The cohort included 322 Korean patients diagnosed with LN between 1985 and 2010. We retrospectively analyzed the clinical and laboratory indices, treatment response, the final renal function, and the biopsy findings. The timing and cumulative risk of developing CKD were identified by Kaplan-Meier methods. The independent risk factors for developing CKD were examined by univariate and multivariate Cox proportional hazards regression analyses. RESULTS: The median followup time after the diagnosis of LN was 84 months. CKD occurs in 22% of the patients within 10 years after the diagnosis of LN. The probability of developing CKD was significantly associated with the onset time of LN (delayed-onset LN vs initial-onset LN; HR 2.904, p = 0.003), deteriorated renal function [an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m(2) body surface area] at the onset of LN (HR 7.458, p < 0.001), relapse of LN after achieving remission (HR 2.806, p = 0.029), and resistance to induction therapy (HR 8.120, p < 0.001). CONCLUSION: Our results demonstrate that delayed-onset LN, a decreased eGFR at the time of LN onset, and the failure to achieve a sustained remission are predictors for the development of CKD in Korean patients with LN.
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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 | 0.000 |
| 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) | 0.001 | 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".