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Record W2039410534 · doi:10.1097/mnh.0000000000000063

End-stage renal disease risk in live kidney donors

2014· article· en· W2039410534 on OpenAlexafffund
Ngan N. Lam, Krista L. Lentine, Amit X. Garg

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2014
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsWestern UniversityInstitute for Clinical Evaluative SciencesLondon Health Sciences Centre
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesCanadian Institutes of Health Research
KeywordsMedicineIncidence (geometry)Kidney donationEnd stage renal diseaseDonationKidneyRelative riskKidney diseaseDiseaseKidney transplantationInternal medicineConfidence interval

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Living kidney donation improves the lives of those with kidney failure, but there are potential risks to the donor. We review two recent publications that describe the long-term risk of end-stage renal disease (ESRD) in living kidney donors. RECENT FINDINGS: One study reported that the long-term risk (median follow-up 15.1 years) of ESRD was, in relative terms, 11-fold higher in living kidney donors compared to healthy nondonors, and suggested a hereditary association since all affected donors were biologically related to their recipients and the causes were predominantly immunological diseases. In a second study, we estimated that the long-term risk (median follow-up 7.6 years) of ESRD was, in relative terms, eight-fold higher in living kidney donors compared to healthy matched nondonors. In both studies, the absolute increase in the 15-year incidence of ESRD from donation was below 0.5%. There are limitations in these studies, which have raised questions about the accuracy of the estimates of risk. SUMMARY: The results of these studies should be discussed with potential living kidney donors with an emphasis on the low 15-year incidence of ESRD following donation. The lifetime incidence of ESRD for donors of different age, race, and other characteristics requires further study.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.642

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 head0.031
GPT teacher head0.295
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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations32
Published2014
Admission routes2
Has abstractyes

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