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Clinical Outcomes after Failed Renal Transplantation—Does Dialysis Modality Matter?

2008· review· en· W1931957718 on OpenAlexaff
Jeffrey Perl, Joanne M. Bargman, Simon Davies, Sarbjit V. Jassal

Bibliographic record

VenueSeminars in Dialysis · 2008
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineDialysisImmunosuppressionHemodialysisPeritoneal dialysisTransplantationHome hemodialysisIntensive care medicinePopulationNephrologyKidney transplantationInternal medicineUrologySurgery

Abstract

fetched live from OpenAlex

Patients returning to dialysis after graft loss (DAGL) are an increasing segment of the end-stage renal disease (ESRD) population. It is unclear whether patients with previous graft loss have equivalent or reduced survival from the time of restarting dialysis when compared with ESRD patients initiating dialysis for the first time. Moreover, the impact of dialysis modality on the survival of patients returning to DAGL is not known. Studies of patients with transplant graft failure returning to hemodialysis (HD) have suggested decreased survival when compared with transplant-naïve dialysis patients, yet some studies of patients with graft failure returning to peritoneal dialysis (PD) have demonstrated equivalent survival. Based on these data, it is unclear whether survival differences may exist between the dialysis modalities, and if they do, whether they can be attributed to either differences in patient characteristics or to factors related to the dialysis modalities. For patients starting back onto dialysis, in whom preservation of residual renal function is important, it is also unclear how immunosuppression reduction or transplant nephrectomy may affect survival. In this review, we will summarize the available literature on survival rates of patients returning to DAGL; compare and contrast survival after initiation of HD and PD and discuss what is known about the impact of transplant nephrectomy and the different approaches to immunosuppression reduction. Practical considerations will be discussed with a specific emphasis on patients treated by PD.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.286
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.379
Teacher spread0.341 · 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
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

Citations34
Published2008
Admission routes1
Has abstractyes

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