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Impairment of renal function after islet transplant alone or islet-after-kidney transplantation using a sirolimus/tacrolimus-based immunosuppressive regimen

2005· article· en· W2004470375 on OpenAlexaboutno aff
Axel Andrès, Christian Toso, Philippe Morel, Sandrine Demuylder-Mischler, Domenico Bosco, Reto M. Baertschiger, Nadine Pernin, Pascal Bucher, Pietro Majno, Léo H. Bühler, Thierry Berney

Bibliographic record

VenueTransplant International · 2005
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsnot available
FundersNational Research Foundation
KeywordsMedicineSirolimusTacrolimusKidney transplantationRenal functionTransplantationRegimenUrologyIsletAlbuminuriaKidneyInternal medicineDiabetes mellitusGastroenterologyEndocrinology

Abstract

fetched live from OpenAlex

The immunosuppressive (IS) regimen based on sirolimus/low-dose tacrolimus is considered a major determinant of success of the Edmonton protocol. This regimen is generally considered safe or even protective for the kidney. Herein, we analyzed the impact of the sirolimus/low-dose tacrolimus combination on kidney function. The medical charts of islet transplant recipients with at least 6 months follow up were reviewed. There were five islet-after-kidney and five islet transplantation alone patients. Serum creatinin, albuminuria, metabolic control markers and graft function were analyzed. Impairment of kidney function was observed in six of 10 patients. Neither metabolic markers nor IS drugs levels were significantly associated with the decrease of kidney function. Although a specific etiology was not identified, some subsets of patients presented a higher risk for decline of kidney function. Low creatinin clearance, albuminuria and long-established kidney graft were associated with poorer outcome.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.013
GPT teacher head0.258
Teacher spread0.246 · 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 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

Citations35
Published2005
Admission routes1
Has abstractyes

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