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Record W2117053752 · doi:10.2215/cjn.03791106

Therapeutic Monitoring of Calcineurin Inhibitors for the Nephrologist

2007· review· en· W2117053752 on OpenAlexaff
Jeffrey Schiff, Edward Cole, Marcelo Cantarovich

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2007
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsRoyal Victoria Regional Health CentreToronto General HospitalMcGill University Health CentreRoyal Victoria HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineCalcineurinDosingImmunosuppressionPharmacokineticsTacrolimusTherapeutic drug monitoringPharmacologyIntensive care medicineNephrologyRenal functionClinical significanceInternal medicineTransplantation

Abstract

fetched live from OpenAlex

The calcineurin inhibitors (CNI) cyclosporine and tacrolimus remain the backbone of immunosuppression for most kidney transplant recipients. Despite many years of experience, protocols that optimize efficacy with minimal toxicity remain a subject of debate. Nevertheless, studies of the pharmacokinetic properties of the CNI, particularly cyclosporine, have led to improved dosing strategies. The purpose of this article is to review the current understanding of CNI pharmacokinetics and its relevance to proper dosing and monitoring of these medications. This article also reviews the trials that have helped to define the optimal dosages and discusses the effect of adjunctive immunosuppressive agents on CNI pharmacokinetics and dosing.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.226
GPT teacher head0.499
Teacher spread0.273 · 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 designNot applicable
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

Citations255
Published2007
Admission routes1
Has abstractyes

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