Role of plasmapheresis in the management of myeloma kidney: A systematic review
Bibliographic record
Abstract
Multiple myeloma complicated by acute renal failure is a diagnosis often encountered by the practicing nephrologist. The role of plasmapheresis in such patients has been of interest for decades. Three randomized controlled trials (RCTs) and multiple observational trials have evaluated the potential role of plasmapheresis in the management of this condition. This systematic review presents the results of these trials regarding survival benefits, recovery from dialysis, and improvement in renal function. A comprehensive search revealed 56 articles. Of these, only 8 articles met our inclusion criteria (3 RCTs, 1 correction of results, and 4 observational trials). Two of the 3 RCTs showed no difference in survival benefit. Two of the 3 RCTs showed a greater percentage of patients stopping dialysis in the intervention group; however, these results were not reproduced in the largest trial. All the studies showed an improvement in renal function for patients receiving plasmapheresis; however, only 2 RCTs and 1 retrospective study showed a statistically significant improvement in renal function among patients who received plasmapheresis in comparison with a control group. Our systematic review does not suggest a benefit of plasmapheresis independent of chemotherapy for multiple myeloma patients with acute renal failure in terms of overall survival, recovery from dialysis, or improvement in renal function.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".