Perspectives From an Onconephrology Interest Group: Conference Report
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: Onconephrology is a new and evolving field that deals with kidney complications in patients with cancer as well as the management of cancer in patients with preexisting kidney disease. With increasing numbers of patients with cancer with kidney-related complications, the field has garnered increased attention. Thus, an annual Greater Toronto Area Onconephrology Interest Group symposium was held in May 2019. The objective of the meeting was to demonstrate the junctures between oncology and nephrology by highlighting recent data regarding (1) kidney impairment in solid organ malignancies, (2) management and treatment of kidney cancer, (3) kidney impairment in hematologic malignancies, (4) malignancy and kidney transplantation, and (5) hyponatremia in patients with cancer. METHODS AND SOURCES OF INFORMATION: Through a structured presentation, the group explored key topics discussed at a Kidney Disease Improving Global Outcomes (KDIGO) Controversies Conference on Onconephrology. Expert opinions, clinical trial findings, and publication summaries were used to illustrate patient and treatment-related considerations in onconephrology. KEY FINDINGS: Kidney complications in patients with cancer are a central theme in onconephrology. An estimated 12% to 25% of patients with solid organ malignancies have chronic kidney disease (CKD), although in certain cancers, the prevalence of CKD is higher. Kidney impairment is also a common complication of some hematologic malignancies. The incidence of renal failure in patients with multiple myeloma is estimated at 18% to 56% and light chain cast nephropathy is seen in approximately 30% of these patients. In addition, there appears to be a bidirectional relationship between kidney cancer and CKD, with some data sets suggesting the risk increases as kidney function declines. Cancer is also of concern in patients with preexisting kidney disease. Kidney transplant recipients have a greater risk of cancer and a higher risk of cancer-related mortality. Kidney complications have also been associated with novel cancer therapies, such as immune checkpoint inhibitors and chimeric antigen receptor (CAR) T-cell therapy. An estimated 2% to 4% of patients initiating an immune checkpoint inhibitor may develop nephrotoxicity, whereas up to 40% of patients on CAR T-cell therapy experience cytokine release syndrome (CRS). Tumor lysis syndrome and electrolyte abnormalities, such as hyponatremia, have also been reported with CAR T-cell therapy. While the incidence and prevalence of hyponatremia vary depending on the cancer type and serum sodium cutoff point, hyponatremia may be seen in up to 46% of patients hospitalized in cancer centers. CONCLUSIONS: Onconephrology is a developing field and the themes arising from this meeting indicate a need for greater collaboration between oncologists and nephrologists. Educational symposia and onconephrology fellowship programs may allow for improved cancer care for patients with kidney disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".