Frailty Screening in Chronic Kidney Disease: Current Perspectives
Bibliographic record
Abstract
George Worthen,1 Karthik Tennankore2 1Department of Medicine, Nova Scotia Health Authority, Halifax, NS, Canada; 2Division of Nephrology, Nova Scotia Health Authority, Halifax, NS, CanadaCorrespondence: Karthik TennankoreDivision of Nephrology, QEII - Dickson Building, Suite 5090 Dickson Building, 5820 University Avenue, Halifax, NS B3H 2Y9, CanadaTel +1 902-473-2099Fax +1 902-473-2675Email KarthikK.Tennankore@nshealth.caAbstract: Frailty has been defined as a state of increased vulnerability as a consequence of deficit accumulation. Frailty screening has not yet been widely implemented into routine nephrology care. Patients with chronic kidney disease (CKD) are at high risk of being frail, and frailty has been associated with worse outcomes in this population. Standard management of CKD, including initiation of renal replacement therapies, may have decreased benefit or potentially cause harm in the presence of frailty, and a variety of interventions for modifying frailty in the CKD population have been proposed. The optimal means of screening for frailty in patients with kidney disease remains unclear. This review highlights the value of frailty screening in CKD by summarizing the outcomes associated with frailty and exploring proposed changes to the management of frail patients with CKD. Finally, we will propose a framework for how to implement frailty screening into standard nephrology care.Keywords: geriatrics, sarcopenia, transplant, dialysis, conservative management
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".