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Long‐term clinical outcomes of nocturnal hemodialysis patients compared with conventional hemodialysis patients post‐renal transplantation

2009· article· en· W2052659010 on OpenAlexafffund
Robert P. Pauly, Reem Asad, James A. Hanley, Andreas Pierratos, Jeffrey S. Zaltzman, Anne Chery, Christopher T. Chan

Bibliographic record

VenueClinical Transplantation · 2009
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsToronto General HospitalHumber River Regional HospitalMcGill UniversityUniversity Health Network
FundersUniversity Health Network
KeywordsMedicineHemodialysisTransplantationDialysisIncidence (geometry)Renal functionKidney transplantationUrologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Nocturnal home hemodialysis (NHD) is a novel dialysis strategy associated with multiple advantages over conventional hemodialysis (CHD). Short- and long-term clinical outcomes of NHD patients after kidney transplantation are unknown. We hypothesized that the incidence of delayed graft function (DGF), patient and graft survival, and post-transplant estimated glomerular filtration rate (eGFR) is better among CHD-transplanted individuals than among those having received NHD. Of 231 NHD patients, 36 underwent renal transplantation between 1994 and 2006 and were matched to 68 transplanted CHD patients with a maximum follow-up of 11.7 yr. The incidence of DGF was not different between the two groups [NHD: 15/35 (42.9%) vs. CHD: 25/68 (36.8%) p = 0.43]. In modeling eGFR pre-transplant weight, donor age and recipient race were most predictive. Dialysis modality prior to transplantation influenced neither the level of eGFR post-transplantation (p = 0.34), nor the rate of eGFR decline. Patient survival was comparable between NHD and CHD groups (log-rank p = 0.91). Based on this analysis, it appeared that the incidence of DGF was similar between NHD- and CHD-transplanted patients and that pre-transplant modality did not impact on the level or rate of deterioration of post-transplant eGFR.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.033
GPT teacher head0.342
Teacher spread0.309 · 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
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

Citations12
Published2009
Admission routes2
Has abstractyes

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