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Record W2053301717 · doi:10.1111/ctr.12266

Peritoneal dialysis versus hemodialysis in patients with delayed graft function

2013· article· en· W2053301717 on OpenAlexaffabout
A. B. R. Thomson, Mike Moser, Caitlyn Marek, Michael Bloch, Corinne Weernink, A Shoker, Patrick Luke

Bibliographic record

VenueClinical Transplantation · 2013
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of SaskatchewanLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicinePeritoneal dialysisHemodialysisDialysisTransplantationUrologyPerioperativeSurgeryKidney transplantationRetrospective cohort studyNephrologyAdverse effectInternal medicine

Abstract

fetched live from OpenAlex

Delayed graft function (DGF) in kidney transplantation affects adverse outcomes. It remains unclear whether the post-transplant dialysis modality alters perioperative or long-term graft outcomes. We performed a retrospective observational quality initiative at two Canadian renal transplant centers, in which DGF occurred in the recipient, necessitating one of peritoneal dialysis (PD) or hemodialysis (HD). There was no difference in baseline factors between patients with post-transplant PD (n = 14) or HD (n = 63). The use of PD was associated with an increased risk of wound infection/leakage (PD 5/14 vs. HD 6/63, p = 0.024), shorter length of hospitalization (13.7 vs. 18.7 d, p = 0.009) and time requiring dialysis post-operatively (6.5 vs 11.0 d, p = 0.043). There were no differences in readmission to hospital within 6 months (4/14 vs. 23/63, p = 0.759), graft loss (0/14 vs. 2/63, p = 1.000) or acute rejection episodes (1/14 vs. 4/63, p = 1.000) at one yr, and GFR did not differ between the PD or HD groups at 30 d (35.7 vs. 33.8 mL/min/m(2), p = 0.731), six months (46.9 vs. 45.5 mL/min/m(2), p = 0.835) or one yr (46.6 vs. 44.5 mL/min/m(2), p = 0.746). Further research is needed to determine which transplant patients are most appropriate to undergo PD catheter removal at the time of transplantation.

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 score0.013
Threshold uncertainty score0.539

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 head0.028
GPT teacher head0.313
Teacher spread0.285 · 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

Citations16
Published2013
Admission routes2
Has abstractyes

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