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Macrophage activation is associated with poorer long‐term outcomes in renal transplant patients

2010· article· en· W1552421277 on OpenAlexaff
Scott O. Grebe, Uwe Kuhlmann, D. Fogl, Valérie A. Luyckx, Thomas Mueller

Bibliographic record

VenueClinical Transplantation · 2010
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineNeopterinDialysisInternal medicineTransplantationGastroenterologyKidney transplantationInflammationC-reactive proteinKidney diseaseHemodialysisImmunologySurgery

Abstract

fetched live from OpenAlex

Long-term graft and patient survival after renal transplantation are largely determined by progression of chronic allograft dysfunction and cardiovascular disease. Inflammation plays a crucial role in both disease processes. We prospectively analyzed the association of early peri-transplant inflammatory burden on long-term outcomes in 144 consecutive deceased donor renal allograft recipients. Single time point and cumulative levels of markers of acute phase response (serum amyloid A [SAA] and C-reactive protein [SCRP]) and macrophage activation (serum and urine neopterin) were measured daily during the immediate post-operative period. Mean patient follow-up was 16 yr. Graft and patient survival rates at one-, five-, and 10-yr were 90%, 70%, and 51%, and 97%, 77%, and 59%, respectively. Graft loss occurred in 90 patients, of whom 71 died with a functioning graft and 19 returned to dialysis. CRP, SAA and neopterin (NEOP) levels were all elevated post-operatively. High levels of NEOP, in contrast to SAA or SCRP, were associated with poorer graft and patient survival (p < 0.05), specifically with death from cardiovascular events and cytomegalovirus IgG positivity. These findings strongly suggest that early post-transplant macrophage activation, as reflected by NEOP levels, is associated with poorer long-term graft and patient survival.

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 score0.015
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.039
GPT teacher head0.357
Teacher spread0.318 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

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