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Record W2888696165 · doi:10.1002/cpdd.607

Assessment of the Effects of Age and Renal Function on Pharmacokinetics of Bazedoxifene in Postmenopausal Women

2018· article· en· W2888696165 on OpenAlexaff
William McKeand, James Ermer, Joan Korth‐Bradley

Bibliographic record

VenueClinical Pharmacology in Drug Development · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsWomen's Health Research Institute
FundersPfizer
KeywordsMedicinePharmacokineticsRenal functionSelective estrogen receptor modulatorPlaceboInternal medicineOsteoporosisImpaired renal functionPostmenopausal womenUrologyEstrogen receptorCancer

Abstract

fetched live from OpenAlex

Abstract Bazedoxifene (BZA), a chemically distinct selective estrogen receptor modulator, has demonstrated efficacy and long‐term safety in phase 3 placebo‐controlled studies for prevention and treatment of osteoporosis. Here, we assessed the potential effects of age and renal function on BZA pharmacokinetics in healthy postmenopausal women (aged 55–84 years; CLcr, 32‐109 mL/min). This was an open‐label, single‐dose, parallel, nonrandomized inpatient study conducted in healthy postmenopausal women and postmenopausal women with impaired renal function. Each subject received a single oral dose of BZA in a 20‐mg tablet. Twenty‐six subjects were enrolled: 8 in each of 3 age groups (55–64 years, 65–74 years, ≥75 years) and 2 (aged 71 and 75 years) with mild renal impairment; all subjects received treatment and completed the study. Age‐related changes in pharmacokinetics were apparent. Although the correlation was modest ( R 2 = 0.28), BZA CL/F decreased steadily with age, such that the oldest group (>75 years) had a mean CL/F 60% less than the youngest group (55–64 years). Over the observed range of CLcr, there was a weak positive correlation ( R 2 = 0.19) between BZA CL/F and CLcr.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.396

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 integrity0.0000.000
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 head0.009
GPT teacher head0.334
Teacher spread0.326 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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