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Record W1935690703 · doi:10.1111/ctr.12639

Comparing 10‐yr renal outcomes in deceased donor and living donor liver transplants

2015· article· en· W1935690703 on OpenAlexaff
Shaifali Sandal, Anthony Almudevar, Sandesh Parajuli, Anirban Bose

Bibliographic record

VenueClinical Transplantation · 2015
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsMcGill University Health Centre
FundersNational Center for Advancing Translational SciencesNational Institutes of HealthGeorgia Clinical and Translational Science AllianceUniversity of Rochester
KeywordsMedicineIncidence (geometry)DialysisRetrospective cohort studyQuartileSurgeryInternal medicineGastroenterologyConfidence interval

Abstract

fetched live from OpenAlex

Few studies have explored whether the type of LT, deceased donor LT (DDLT) or living donor LT (LDLT), impacts long-term renal outcomes. We performed a retrospective analysis of 220 LT recipients at our institution to study their renal outcomes at 10 yr. Exclusion criteria were age ≤ 18 yr, graft survival ≤ 6 months, and multiorgan transplants; 108 DDLTs and 62 LDLTs were eligible. At baseline, DDLTs had a lower eGFR than LDLTs and 10.2% of DDLTs were on dialysis as compared to 0% of LDLTs. At 10 yr, seven DDLT and three LDLT recipients required dialysis or renal transplant (p = 0.75). In recipients with graft survival >6 months, DDLTs had a slower decline in eGFR as compared to LDLTs (p < 0.01). Among LDLTs, the decline in eGFR continued over the entire 10-yr period, whereas among DDLTs, the decline in eGFR slowed significantly after six months (p = 0.01). This difference between the two groups was not seen among patients in the highest quartile of baseline eGFR. Patient survival and graft survival were similar. In conclusion, the incidence of end-stage renal disease was similar in both DDLT and LDLT patients, but LDLT recipients seem to have a more sustained decline in eGFR when compared with DDLT recipients.

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)0.001
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 score0.006
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.135
GPT teacher head0.369
Teacher spread0.235 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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