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Immunosuppression without calcineurin inhibition: optimization of renal function in expanded criteria donor renal transplantation

2009· article· en· W1969318433 on OpenAlexaff
Patrick Luke, Christopher Nguan, David Horovitz, Laura Gregor, Jeff Warren, Andrew A. House

Bibliographic record

VenueClinical Transplantation · 2009
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of British ColumbiaLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineCalcineurinImmunosuppressionPrednisoneUrologyTransplantationDiscontinuationSirolimusRenal functionMycophenolic acidAdverse effectInternal medicineKidney transplantationSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: To assess the efficacy of calcineurin inhibitor (CNI)-free immunosuppression vs. calcineurin-based immunosuppression in patients receiving expanded criteria donor (ECD) kidneys. PATIENT AND METHODS: Thirteen recipients of ECD kidneys were enrolled in this pilot study and treated with induction therapy and maintained on sirolimus, mycophenolate mofetil (MMF) and prednisone. A contemporaneous control group was randomly selected comprised of 13 recipients of ECD kidneys who had been maintained on CNI plus MMF and prednisone. RESULTS: For the study group vs. the control group, two-yr graft survival was 92.3% vs. 84.6% (p = NS), two-yr patient survival was 100% vs. 92.3% (p = NS) and the acute rejection rates were 23% vs. 31% (p = NS), respectively. Renal function was significantly better in the study group compared with control up to the six-month mark, after which, it remained numerically but not statistically significant. Complications were more common in the study group, but serious adverse events requiring discontinuation were rare. CONCLUSION: This pilot study demonstrates that CNI-free regimens can be safely implemented in patients receiving ECD kidneys with excellent two-yr patient and graft survival and good renal allograft function. Longer follow-up in larger randomized controlled trials are necessary to establish these findings.

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 score0.487
Threshold uncertainty score0.965

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 integrity0.0000.000
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 head0.048
GPT teacher head0.378
Teacher spread0.331 · 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

Citations11
Published2009
Admission routes1
Has abstractyes

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