Management of acute kidney injury in symptomatic multiple myeloma
Bibliographic record
Abstract
Symptomatic multiple myeloma is commonly complicated by acute kidney injury through various mechanisms. The most frequent is the precipitation of monoclonal free light chains with uromodulin in the distal tubules, defining light chain cast nephropathy. Early diagnosis and identification of the cause of acute kidney injury are required for optimizing management and avoiding chronic kidney injury that strongly affects quality of life and patient survival. In light chain cast nephropathy, often manifesting with severe acute kidney injury, renal recovery requires urgent intervention based on vigorous rehydration, correction of precipitating factors, and efficient anti-plasma cell chemotherapy to rapidly reduce the secretion of nephrotoxic free light chains. Currently, the association of the proteasome inhibitor bortezomib with high-dose dexamethasone is the standard regimen in newly diagnosed patients. The addition of another drug such as cyclophosphamide or an immunodulatory agent may improve free light chain response but raises tolerance concerns in frail patients. Further studies are warranted to confirm the role of anti-CD38 monoclonal antibodies, whose efficacy and tolerance have been documented in patients without renal impairment. Despite controversial results from randomized studies, recent data suggest that in patients with light chain cast nephropathy and acute kidney injury requiring dialysis, the combination of chemotherapy with free light chain removal through high-cutoff hemodialysis may increase renal response recovery rates. Kidney biopsy may be helpful in guiding management and assessing renal prognosis that appears to depend on the extent of cast formation and interstitial fibrosis/tubular atrophy. Because of continuous improvement in life expectancy of patients with multiple myeloma, renal transplantation is likely to be increasingly considered in selected candidates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.003 | 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".