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Record W2882991814 · doi:10.1002/psp4.12338

Physiologically Based Pharmacokinetic Approach to Determine Dosing on Extracorporeal Life Support: Fluconazole in Children on<scp>ECMO</scp>

2018· article· en· W2882991814 on OpenAlexaff
Kevin Watt, Michael Cohen‐Wolkowiez, Jeffrey S. Barrett, Michael Sevestre, Ping Zhao, Kim L. R. Brouwer, Andrea N. Edginton

Bibliographic record

VenueCPT Pharmacometrics & Systems Pharmacology · 2018
Typearticle
Languageen
FieldMedicine
TopicAntibiotics Pharmacokinetics and Efficacy
Canadian institutionsUniversity of WaterlooBlackberry (Canada)
FundersNational Center for Advancing Translational SciencesNational Institute of Child Health and Human DevelopmentNational Institute of Allergy and Infectious DiseasesNational Institute of General Medical SciencesBiomedical Advanced Research and Development AuthorityNational Institutes of HealthEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentChildren's Hospital of PhiladelphiaThrasher Research Fund
KeywordsLife supportDosingExtracorporealPharmacokineticsFluconazoleExtracorporeal membrane oxygenationMedicinePharmacologyIntensive care medicineAnesthesiaAntifungalInternal medicine

Abstract

fetched live from OpenAlex

Extracorporeal life support (e.g., dialysis, extracorporeal membrane oxygenation (ECMO)) can affect drug disposition, placing patients at risk for therapeutic failure. In this population, dose selection to achieve safe and effective drug exposure is difficult. We developed a novel and flexible approach that uses physiologically based pharmacokinetic (PBPK) modeling to translate results from ECMO ex vivo experiments into bedside dosing recommendations. To determine fluconazole dosing in children on ECMO, we developed a PBPK model, which was validated using fluconazole pharmacokinetic (PK) data in adults and critically ill infants. Next, an ECMO compartment was added to the PBPK model and parameterized using data from a previously published ex vivo study. Simulations using the final ECMO PBPK model reasonably characterized observed PK data in infants on ECMO, and the model was used to derive dosing in children on ECMO across the pediatric age spectrum. This approach can be generalized to other forms of extracorporeal life support (ECLS), such as dialysis.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.044
GPT teacher head0.324
Teacher spread0.280 · 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 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

Citations51
Published2018
Admission routes1
Has abstractyes

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