Differences in Frailty by Sex in Kidney Transplant Candidates
Bibliographic record
Abstract
Background: Frailty prevalence is higher in women, despite the observed protective effect of female sex on mortality in the general population. Understanding whether there are differences in perceived frailty by sex and the differential impact of frailty on outcomes for males versus females is crucial to avoid a sex disparity within the transplant assessment process. Methods: We analyzed initial frailty assessments for patients enrolled in a multicenter prospective cohort study across 6 kidney transplant referral centers. Frailty was assessed using the Frailty Phenotype (FP; 3 of slowness, weight loss, low activity, exhaustion, and weakness), a Frailty Index (FI; including 37 variables across the domains of social function/cognition, function, mobility, and comorbidity), and the Clinical Frailty Scale (CFS, based on clinical judgement). Assessments were performed prior to or shortly after waitlisting. Prevalence of frailty as measured by the FP, FI, and CFS was reported. An unadjusted cox survival analysis (separately for males and females) was used to assess the effect of frailty on time to death or withdrawal from the waitlist among activated patients. Results: A total of 767 unique patients had frailty assessments performed between 2016-2021. Patients were predominantly of male sex (64%), white race (82%) and had a mean age of 54+/-14. The prevalence of frailty for women was not significantly higher by the FP (16% vs 13%, p=0.15) or the FI (48% vs 46%, p=0.38), but was by the CFS (17% vs 12%, p=0.05). Among 325 activated patients, frailty by the CFS was significantly associated with death/withdrawal for men (HR 2.59; 95% CI 1.16-6.79) but not women (HR 1.41; 95% CI 0.48-4.18). Conclusions: The prevalence of frailty was higher in females when measured by the CFS, but not by a transplant specific FI or the FP. Despite this, frailty was not significantly associated with mortality/withdrawal from the waitlist for female individuals, emphasizing the need to critically evaluate judgement based frailty assessments and their role in the transplant evaluation process. Funding: Government Support - Non-U.S.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".