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Record W1996447794 · doi:10.1097/mot.0b013e32832e6f7b

Renal-sparing strategies in cardiac transplantation

2009· review· en· W1996447794 on OpenAlexaff
Finn Gustafsson, Heather J. Ross

Bibliographic record

VenueCurrent Opinion in Organ Transplantation · 2009
Typereview
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity Health NetworkToronto General Hospital
Fundersnot available
KeywordsMedicineCalcineurinTransplantationEverolimusDiscontinuationUrologyRenal functionRegimenSirolimusInternal medicineCardiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Renal dysfunction due to calcineurin inhibitor (CNI) toxicity is a major clinical problem in cardiac transplantation. The aim of the article is to review the efficacy and safety of various renal sparing strategies in cardiac transplantation. RECENT FINDINGS: Small studies have documented that late initiation of CNI is safe in patients treated with induction therapy at the time of transplantation. Use of mycophenolate is superior when compared with azathioprine to allow for CNI reduction. More substantial reduction in CNI levels is safe and effective with the introduction of sirolimus or everolimus. However, studies that use very early CNI discontinuation have found an increased risk of allograft rejection, and this strategy requires further study before it can be routinely recommended. CNI discontinuation late after cardiac transplantation seems more effective than CNI reduction in terms of preserving renal function. Patients with longstanding CNI treatment or proteinuria are less likely to respond favourably to a switch from a CNI-based regimen to a proliferation signal inhibitor-based regimen. SUMMARY: Each cardiac transplant recipient with renal dysfunction must be individually evaluated with respect to degree of renal dysfunction, proteinuria and rejection risk and a renal sparing strategy chosen accordingly.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.857
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
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)0.0000.000

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 head0.114
GPT teacher head0.433
Teacher spread0.319 · 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 designOther design
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

Citations15
Published2009
Admission routes1
Has abstractyes

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