Is It Time to Integrate Frailty Assessment in Onconephrology?
Bibliographic record
Abstract
Onconephrology has emerged as a novel sub-specialty of nephrology dedicated to the intersection between the kidney and cancer. This intersection is broad and includes a number of important areas of focus, including concurrent chronic kidney disease (CKD) and cancer, acute kidney complications of cancer, and cancer-treatment-induced nephrotoxicity. The importance of onconephrology is even more evident when considering the global growth in the population of older adults, many of whom are living with some degree of frailty. Furthermore, a considerable proportion of older adults have CKD (some of whom eventually progress to kidney failure) and are at high risk of developing solid tumour and hematologic malignancies. Specific to kidney disease, the association between frailty status and kidney disease has been explored in depth, and tools to capture frailty can be used to guide the management and prognostication of older adults living with kidney failure. Whilst there is emerging data regarding the assessment and impact of frailty in onconephrology, there remains a relative paucity of knowledge within this topic. In this article, we evaluate the definition and operationalization of frailty and discuss the significance of frailty within onconephrology. We review evidence on current approaches to assessing frailty in onconephrology and discuss potential developments and future directions regarding the utilization of frailty in this patient population. A greater awareness of the intersections and interactions between frailty and onconephrology and further efforts to integrate frailty assessment in onconephrology to optimize the delivery of realistic and goal-directed management strategies for patients is needed.
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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 | 0.027 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".