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Record W2069218619 · doi:10.1097/ftd.0b013e3181b385ba

Hypoglycemics: Pharmacokinetic Considerations During Pregnancy

2009· review· en· W2069218619 on OpenAlexafffund
Chagit Klieger, Erika Pollex, Aleksey Kazmin, Gideon Koren

Bibliographic record

VenueTherapeutic Drug Monitoring · 2009
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsPharmacokineticsMedicinePregnancyTransplacentalDrugPharmacologyPharmacodynamicsFetusPlacentaDrugs in pregnancyDiabetes mellitusGestational diabetesGestationEndocrinologyBiology

Abstract

fetched live from OpenAlex

A wide range of physiological and hormonal changes occur during pregnancy. Most begin early in the first trimester and increase by the last trimester. These changes can significantly affect pharmacokinetics and pharmacodynamics of drugs and thus may alter their safety and efficacy. Approximately 5% of pregnant women are affected by some form of diabetes, with gestational diabetes being the most prevalent. Several classes of antidiabetic drugs are currently available for the treatment of diabetes, including human insulin, its short and long analogues, and oral hypoglycemic agents. Maternal and fetal responses to these drugs can be affected by changes in absorption, distribution, and elimination in both the mother and the placental-fetal unit. This can dictate the amount of drug that can cross and the amount that is metabolized or eliminated by the placenta. Further studies are needed on the safety of antidiabetic drugs in pregnancy to clarify the extent of their transplacental passage. Specifically, in vitro placental perfusion studies in combination with controlled trials and cord blood measurements can provide insight in to the pharmacokinetics of drug transport across the placenta. This article reviews common types of antidiabetic drugs, focusing on pharmacokinetic considerations that need to be incorporated into the decision on treatment in pregnancy.

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 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 score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.092
GPT teacher head0.398
Teacher spread0.306 · 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

Citations10
Published2009
Admission routes2
Has abstractyes

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