Looking backward: a review of the treatment of systemic lupus erythematosus in end-stage renal disease after a quarter of century
Bibliographic record
Abstract
Sir, It has now been nearly a quarter of a century since we first reported that the activity of systemic lupus erythematosus (SLE) often becomes quiescent after progression to end-stage renal disease (ESRD) and that the survival rates of those patients were nearly identical to ESRD patients without SLE [1]. I suppose that is why it is so disheartening to read that, despite the great technological advances since our first report, Siu et al. [2] report a mortality rate of 4.3 times the rate of ESRD patients with chronic GN. How can this be? It may have been the demographics. Our patients were younger (28.1 vs 40.8 years), more likely to be women (28/5 vs 13/5), Caucasian, received more transplants (43 vs only 28%) and most notably only 2/28 of our patients underwent continuous ambulatory peritoneal dialysis (CAPD), which was just beginning to be recognized as a viable treatment for ESRD. Yet our patients were more anaemic since erythropoietin had not been discovered then. Similarly, our patients did not achieve the excellent KT/Vs that the patients of Dr Siu achieved. Furthermore, since there were no real differences in survival between CAPD, transplantation and haemodialysis in our data (Figure 1), the poor survival of Dr Siu's patients becomes even more striking. I cannot help wondering if the prolonged use of immunosuppression may have played a role. We were very aggressive in withdrawal from immunosuppression and indeed we found that only a small minority of patients (3/28) required any immunosuppression. That appears to be in stark contrast to the 16/18 patients who continued on immunosuppression in the report of Dr Siu, despite the fact that he reports that only half (9/18) were interpreted to show any disease activity. One of the purposes of our original report was to encourage the aggressive withdrawal of immunosupressants and I am puzzled by the rationale of continuing such immunosuppression in the absence of clinical disease. Although only one of their death cases was of an infectious nature (fungal peritonitis), the remainder were due to vascular complications, yet we now know that the quality and quantity of immunosuppression result in endothelial dysfunction [3], which promotes vascular disease and which may be responsible for the high incidence of graft coronary artery disease in transplants [4].
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".