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Association Between Pretransplant Dialysis Modality and Kidney Transplant Outcomes

2022· review· en· W4306910158 on OpenAlexaff
Tanun Ngamvichchukorn, Chidchanok Ruengorn, Kajohnsak Noppakun, Kednapa Thavorn, Brian Hutton, Manish M. Sood, Greg Knoll, Surapon Nochaiwong

Bibliographic record

VenueJAMA Network Open · 2022
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsModality (human–computer interaction)DialysisMedicineKidney transplantIntensive care medicineTreatment modalityKidney transplantationInternal medicineKidneyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Importance: The benefits and disadvantages of different pretransplant dialysis modalities and their posttransplant outcomes remain unclear in contemporary kidney transplant care. Objective: To summarize the available evidence of the association of different pretransplant dialysis modalities, including hemodialysis and peritoneal dialysis (PD), with posttransplant outcomes. Data Sources: MEDLINE, Embase, PubMed, Cochrane Library, Scopus, CINAHL, and gray literature were searched from inception to March 18, 2022 (updated to April 1, 2022), for relevant studies and with no language restrictions. Study Selection: Randomized clinical trials and nonrandomized observational (case-control and cohort) studies that investigated the association between pretransplant dialysis modality and posttransplant outcomes regardless of age or donor sources (living or deceased) were abstracted independently by 2 reviewers. Data Extraction and Synthesis: Following Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology reporting guidelines, 2 reviewers independently extracted relevant information using a standardized approach. Random-effects meta-analysis was used to estimate pooled adjusted hazard ratio (HR) or odds ratio and 95% CI. Main Outcomes and Measures: Primary outcomes included all-cause mortality, overall graft failure, death-censored graft failure, and delayed graft function. Secondary outcomes included acute rejection, graft vessel thrombosis, oliguria, de novo heart failure, and new-onset diabetes after transplant. Results: The study analyzed 26 nonrandomized studies (1 case-control and 25 cohort), including 269 715 patients (mean recipient age range, 14.5-67.0 years; reported proportions of female individuals, 29.4%-66.9%) whose outcomes associated with pretransplant hemodialysis vs pretransplant PD were compared. No significant difference, with very low certainty of evidence, was observed between pretransplant PD and all-cause mortality (13 studies; n = 221 815; HR, 0.92 [95% CI, 0.84-1.01]; P = .08) as well as death-censored graft failure (5 studies; n = 96 439; HR, 0.98 [95% CI, 0.85-1.14]; P = .81). However, pretransplant PD was associated with a lower risk for overall graft failure (10 studies; n = 209 287; HR, 0.96 [95% CI, 0.92-0.99]; P = .02; very low certainty of evidence) and delayed graft function (6 studies; n = 47 118; odds ratio, 0.73 [95% CI, 0.70-0.76]; P < .001; low certainty of evidence). Secondary outcomes were inconclusive due to few studies with available data. Conclusions and Relevance: Results of the study suggest that pretransplant PD is a preferred dialysis modality option during the transition to kidney transplant. Future studies are warranted to address shared decision-making between health care professionals, patients, and caregivers as well as patient preferences.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.082
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.082
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0050.008
Science and technology studies0.0000.001
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.073
GPT teacher head0.362
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations26
Published2022
Admission routes1
Has abstractyes

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