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Basiliximab lowers the cyclosporine therapeutic threshold in the early post‐kidney transplant period

2005· article· en· W2097082903 on OpenAlexaff
F. Balbontin, B. Kiberd, Albert D. Fraser, Mathew B. Kiberd, Joseph Lawen

Bibliographic record

VenueClinical Transplantation · 2005
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsQueen Elizabeth II Health Sciences Centre
Fundersnot available
KeywordsBasiliximabMedicinePrednisoneUrologyKidney transplantationTransplantationKidneyCiclosporinImmunosuppressionGastroenterologyInternal medicine

Abstract

fetched live from OpenAlex

Early adequate cyclosporine exposure has been shown to predict low acute rejection rate in kidney transplantation. The aim of this study is to determine the importance of exceeding the early cyclosporine therapeutic exposure threshold with basiliximab induction. A retrospective analysis of 166 first cadaveric and non-identical live donor transplant recipients treated with or without basiliximab induction, Neoral, mycophenolate mofetil and prednisone, was performed. Adequate exposure was defined as a 2-h post-Neoral dose cyclosporine level (C2) >1700 ng/mL at day 3. The primary outcome was acute rejection within the first 6 months. In the no basiliximab (control) group (n = 74), rejection occurred in 23% (17 of 74) of recipients and was strongly associated with low cyclosporine exposure on day 3. Acute rejection occurred in 38% (11 of 29) with C2 <1700 ng/mL compared with 13% (six of 45) with C2 >/=1700 ng/mL (p = 0.014). In the basiliximab group (n = 92), rejection occurred in only 11% (10 of 92) of recipients and did not correlate with cyclosporine exposure. Acute rejection occurred in 10% (four of 40) with C2 <1700 ng/mL compared with 12% (six of 52) with C2 >/=1700 ng/mL (p = 0.81). Therefore achieving cyclosporine therapeutic targets by day 3 may not be required when anti-IL2 receptor antibody induction is used.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.045
GPT teacher head0.357
Teacher spread0.312 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

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