What Have We Learned about Optimal Induction Therapy for Lupus Nephritis (III through V) from Randomized, Controlled Trials?
Bibliographic record
Abstract
From 2001 to 2007, five randomized, controlled trials (RCT) of induction protocols for the treatment of lupus nephritis (stages III through V) seem to have captured the interest of the nephrology community (1–5). It is not difficult to critique analytically the shortcomings in study design of these previously published RCT; however, it is a more daunting task to try to assess dispassionately the strengths and weaknesses of these RCT to synthesize a simple, accurate, and useful therapeutic strategy for a practicing nephrologist. Table 1 lists five RCT and some of the salient features about these studies. The first study was reported in 2001 by Illei et al. (1) from the National Institutes of Health (NIH) and represents an extension of a previously reported RCT with extended follow-up design (6). This was a three-armed study (intravenous cyclophosphamide 1 g/m2 monthly for 6 mo and then every 3 mo for 18 mo versus intravenous methylprednisolone 1 g/m2 monthly for 12 mo versus both therapies, combined) of 82 patients, 65 of whom completed the study protocol. The major strengths of this study were that it is an RCT of 82 patients who had lupus with biopsy-proven type III or type IV proliferative lesions with an extended follow-up period (median 132 mo). Two major weaknesses of the study were that it was three armed (reducing its power) and had a very high rate of secondary crossover (63%) to cyclophosphamide treatment among patients who initially were assigned to the methylprednisolone group. The authors indicated that this explains the apparent equity in long-term renal outcomes in the intention-to-treat analysis for the cyclophosphamide and methylprednisolone groups (doubling of serum creatinine, progression to ESRD, and death); however, a composite outcome for treatment failure (death, doubling in serum creatinine, and need for additional immunosuppressive therapy …
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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 | 0.340 | 0.607 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".