Advanced Chronic Kidney Disease in Lupus Nephritis: Is Dialysis Inevitable?
Bibliographic record
Abstract
OBJECTIVE: Advanced chronic kidney disease (CKD) carries an increased risk for progression to endstage renal disease (ESRD). We aimed to determine the rate of progression and the factors that drive the decline of renal function in lupus nephritis (LN). METHODS: ). All individuals were followed until progression to ESRD or the last visit and were divided into "progressors" and "non- progressors." Demographic, clinical, immunological, and therapeutic variables were compared at baseline. Multivariable Cox regression analysis (both time-dependent and independent) was performed to identify predictors for progression. RESULTS: One hundred eighteen patients (74 CKD 3b and 44 CKD 4) were included. Forty-five patients progressed (29 to ESRD and 16 from CKD 3b to CKD 4) after 6 years on average. No significant decline in the renal function was observed in 73 patients ("non-progressors") after 10 years on average. Active serology (high anti-dsDNA titers and low complements C3/C4) at the time of CKD diagnosis and any increase of the daily prednisone dose after baseline were strongly associated with progression. Treatment with renin angiotensin system (RAS) blockers was associated with less risk for progression. CONCLUSION: Dialysis is not inevitable in LN-related advanced CKD because 62% of our patients did not progress over 10 years of followup on average. Certain predictors were identified to affect progression to ESRD.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".