A Comparison of Frailty Measures Among Patients Referred for Kidney Transplantation
Bibliographic record
Abstract
Background: Frailty is highly prevalent in patients referred for kidney transplantation. While the Fried Frailty Phenotype (FP) is widely used, less is known about other frailty assessment tools. We assessed and compared the prevalence of frailty using three tools in kidney transplant waitlist candidates. Methods: Kidney transplant waitlist candidates were prospectively enrolled from five centers from June 2016-Feb 2020. Frailty was primarily defined using the FP as three or more of slowness (using walk time), weakness (using grip strength), weight loss, low activity or exhaustion (the latter three using questionnaires). Secondary tools included a Frailty Index (FI) consisting of 37 variables across the domains of social function/cognition, function, mobility and comorbidity, and the Clinical Frailty Scale (CFS), a frailty screen based on clinician gestalt that ranges from 1 (very fit) to 8 (very severely frail). We used adjusted logistic regression to identify factors associated with frailty measured by the FP. Area under the receiver-operator characteristics (ROC) curves were calculated to compare the FP to the FI and CFS. Results: Of 542 enrolled patients, 64% were male, 80% were white, and the mean age was 54±14. The prevalence of frailty by the FP was 16%; it was 27% for those >65 years old. Of the FP components, low grip strength (41%), and exhaustion (36%) were the most prevalent. Using an established cut point of 0.25 yielded a prevalence of 38% by the FI (46% for those >65). Using a cut-off of 5 on the CFS (mildly frail), frailty prevalence was 4% (7% for those >65). The mean FI score was 0.23±0.14 (max 0.70) and median CFS score was 3 (IQR 2,3) or “managing well”. Diabetes (adjusted odds ratio; aOR 2.0, 95% CI 1.0, 3.8), and cerebrovascular disease (aOR 3.3 95% CI 1.3, 8.5) were associated with frailty defined by the FP. Area under the ROC curve for the FP and FI/CFS were 0.86 (good) and 0.69 (poor) respectively. Conclusions: The prevalence of frailty varies using different measurement tools and there are differences in perceived (CFS) versus measured (FP/FI) frailty among patients referred for transplantation. Determining which tool is most associated with outcomes for waitlisted patients is a future objective of this study.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".