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Record W4309341172 · doi:10.1016/j.ekir.2022.11.002

Older Age is Associated With Lower Utilization of Living Donor Kidney Transplant

2022· article· en· W4309341172 on OpenAlexaffabout
Afsaneh Raissi, Aarushi Bansal, Oladapo Ekundayo, Sehajroop Bath, Nathaniel Edwards, Olusegun Famure, S. Joseph Kim, István Mucsi

Bibliographic record

VenueKidney International Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineHazard ratioConfidence intervalOdds ratioKidney transplantRetrospective cohort studyProportional hazards modelCohortLogistic regressionCohort studyKidney transplantationYoung adultInternal medicineKidney

Abstract

fetched live from OpenAlex

Introduction: Older adults (65 years or older) constitute a substantial and increasing proportion of patients with kidney failure, potentially needing kidney replacement therapy. Living donor kidney transplant (LDKT) offers superior outcomes for suitable patients of all ages. However, exploring LDKT and finding a living donor could be challenging for older adults. Here, we assessed the association between age and utilization of LDKT and assessed effect modification of key variables such as ethnicity and language. Methods: This is a retrospective cohort study of patients with kidney failure referred for kidney transplant (KT) assessment in Toronto between January 2006 and December 2013. The association between age and having a potential living donor identified was assessed using logistic regression and the association between age and the receipt of LDKT was assessed using Cox proportional hazards models. Results: = 0.001, for middle-aged and older adults, respectively.). Conclusion: Age is an independent predictor of receiving LDKT. Considering that nearly 90% of patients with kidney failure in Canada are >45 years of age, these results point to important and potentially modifiable age-related barriers to LDKT.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.996

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)0.0050.000

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 head0.022
GPT teacher head0.286
Teacher spread0.264 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2022
Admission routes2
Has abstractyes

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