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Record W2333055671 · doi:10.1097/ftd.0b013e3182028b23

Development and Validation of Limited Sampling Strategies for Tacrolimus and Mycophenolate in Steroid-Free Renal Transplant Regimens

2010· article· en· W2333055671 on OpenAlexaff
Éric Poulin, Erica D. Greanya, Nilufar Partovi, R. Jean Shapiro, Mai Al-Khatib, Mary H. H. Ensom

Bibliographic record

VenueTherapeutic Drug Monitoring · 2010
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaVancouver Coastal Health
Fundersnot available
KeywordsMycophenolic acidTacrolimusMedicineUrologyMycophenolateArea under the curvePopulationRenal transplantPharmacokineticsKidney transplantationInternal medicineTransplantation

Abstract

fetched live from OpenAlex

PURPOSE: 1) To develop and validate limited sampling strategies (LSSs) for tacrolimus (TAC) and mycophenolic acid (MPA) in renal transplant recipients not receiving corticosteroids; and 2) to evaluate predictive performance of published LSSs (for steroid-based regimens) in a steroid-free population. METHODS: On administration of steady-state morning TAC and mycophenolate mofetil doses, 12-hour serial blood samples from 28 stable renal transplant recipients were collected and measured by validated high-performance liquid chromatography methods and area under the curve (AUC) by trapezoidal rule. TAC LSSs were developed and validated by multiple regression analysis by a two-group method (index n = 18; validation n = 10) and MPA LSSs by the jackknife method (n = 28). Potential LSSs were those with r ≥ .8 (TAC) or r ≥ 0.7 (MPA) and < 3 time points within 2 hours (TAC) or 4 hours (MPA) postdose. Predictive performance was calculated and other published TAC and MPA LSSs tested using preset criteria for bias and precision of within ± 15%. RESULTS: For TAC, three three-concentration, one two-concentration, and one one-concentration model met preset criteria. The best equations were: TAC AUC = 10.338 + 7.739C0 + 3.589C2 (r = 0.956, bias = -3.4%, precision = 4.7%) and TAC AUC = 29.479 + 5.016C2 (r = 0.862, bias = 3.2%, precision = 9.7%). For MPA, only one model was identified: MPA AUC = 9.328 + 1.311C1 + 1.455C2 + 2.901C4 (r = 0.838, bias = -3.8%, precision = 14.9%). One published TAC (and no MPA) LSS in renal transplant recipients on steroid-based regimens met criteria. CONCLUSIONS: To the authors' knowledge, these LSSs are the first to be developed and validated in steroid-free renal transplant recipients and can be used to accurately predict TAC and MPA AUCs for steroid-free regimens. Because the commonly used MPA LSS is based on a steroid regimen and not predictive for steroid-free patients, the newly derived MPA LSS is being applied at the authors' institution. Other renal transplant centers may also wish to validate this equation in their own patients.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.049
GPT teacher head0.316
Teacher spread0.267 · 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 designBench or experimental
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

Citations16
Published2010
Admission routes1
Has abstractyes

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