Bibliographic record
Abstract
PURPOSE OF REVIEW: Renal failure is a frequent complication of multiple myeloma and portends a poor prognosis. Plasmapheresis has been suggested as an adjunct to chemotherapy to halt or reverse renal injury associated with multiple myeloma. The purpose of this article is to review the rationale for using plasmapheresis for this indication and then provide a discussion of the evidence regarding its use. RECENT FINDINGS: The outcome of patients with multiple myeloma has improved considerably in recent years, mostly owing to the introduction of new highly effective chemotherapeutic agents. However, patients with renal failure who do not recover independent renal function continue to have very poor prognosis. Recent evidence now indicates that an early and sustained reduction in circulating free light chains (FLCs) is associated with improved renal recovery in patients with myeloma kidney. Extracorporeal removal of FLCs with plasmapheresis, or other techniques, can achieve rapid and sustained reduction in serum FLC concentration in patients with acute myeloma kidney. Unfortunately, there is currently no convincing evidence in the literature that the addition of mechanical removal of FLC to standard chemotherapy translates into clinical benefits for patients. SUMMARY: Plasmapheresis is theoretically attractive as a means of rapidly lowering serum FLC burden in the hope of reducing nephrotoxicity in patients with multiple myeloma. However, the role of plasmapheresis in improving renal prognosis and patient survival remains to be demonstrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".