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Record W3169066077 · doi:10.34067/kid.0001892021

Prevalence of Frailty in Patients Referred to the Kidney Transplant Waitlist

2021· article· en· W3169066077 on OpenAlex
George Worthen, Amanda J. Vinson, Héloïse Cardinal, Steve Doucette, Nessa Gogan, Lakshman Gunaratnam, Tammy Keough-Ryan, Bryce Kiberd, Bhanu Prasad, Kenneth Rockwood, Laura Sills, Rita S. Suri, Navdeep Tangri, Michael Walsh, Kenneth A. West, Seychelle Yohanna, Karthik Tennankore

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueKidney360 · 2021
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsOrthopaedic Innovation CentreMcGill University Health CentreRegina General HospitalMcMaster UniversityLondon Health Sciences CentreHorizon Health NetworkCentre Hospitalier de l’Université de MontréalNova Scotia Health AuthorityDalhousie University
FundersCanadian Institutes of Health ResearchKidney Foundation of CanadaAstellas Pharma Canada
KeywordsMedicineFrailty IndexCohortReceiver operating characteristicProportional hazards modelInternal medicineGerontology

Abstract

fetched live from OpenAlex

Abstract Key Points Frailty prevalence varies for the Frailty Phenotype, a frailty index, and the Clinical Frailty Scale in transplant candidates. Agreement between these measures for determining frailty status was variable, suggesting they measure different aspects of frailty. The frailty index and the Clinical Frailty Scale were associated with a shorter time to death or waitlist withdrawal in an unadjusted analysis. Background Comparisons between frailty assessment tools for waitlist candidates are a recognized priority area for kidney transplantation. We compared the prevalence of frailty using three established tools in a cohort of waitlist candidates. Methods Waitlist candidates were prospectively enrolled from 2016 to 2020 across five centers. Frailty was measured using the Frailty Phenotype (FP), a 37-variable frailty index (FI), and the Clinical Frailty Scale (CFS). The FI and CFS were dichotomized using established cutoffs. Agreement was compared using κ coefficients. Area under the receiver operating characteristic (ROC) curves were generated to compare the FI and CFS (treated as continuous measures) with the FP. Unadjusted associations between each frailty measure and time to death or waitlist withdrawal were determined using an unadjusted Cox proportional hazards model. Results Of 542 enrolled patients, 64% were male, 80% were White, and the mean age was 54±14 years. The prevalence of frailty by the FP was 16%. The mean FI score was 0.23±0.14, and the prevalence of frailty was 38% (score of ≥0.25). The median CFS score was three (IQR, 2–3), and the prevalence was 15% (score of ≥4). The κ values comparing the FP with the FI (0.44) and CFS (0.27) showed fair to moderate agreement. The area under the ROC curves for the FP and FI/CFS were 0.86 (good) and 0.69 (poor), respectively. Frailty by the CFS (HR, 2.10; 95% CI, 1.04 to 4.24) and FI (HR, 1.79; 95% CI, 1.00 to 3.21) was associated with death or permanent withdrawal. The association between frailty by the FP and death/withdrawal was not statistically significant (HR, 1.78; 95% CI, 0.79 to 3.71). Conclusion Frailty prevalence varies by the measurement tool used, and agreement between these measurements is fair to moderate. This has implications for determining the optimal frailty screening tool for use in those being evaluated for kidney transplant.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.261
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it