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Record W2611085069 · doi:10.1080/17425255.2017.1325873

Pharmacokinetic and pharmacodynamic alterations in older people with dementia

2017· review· en· W2611085069 on OpenAlexaff
Emily Reeve, Shanna Trenaman, Kenneth Rockwood, Sarah N. Hilmer

Bibliographic record

VenueExpert Opinion on Drug Metabolism & Toxicology · 2017
Typereview
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsNova Scotia Health AuthorityDalhousie University
Fundersnot available
KeywordsDementiaMedicinePharmacodynamicsPharmacokineticsQuality of life (healthcare)GerontologyIntensive care medicinePharmacologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: The number of people with dementia internationally is increasing. Older adults with dementia are prescribed multiple medications, both to treat dementia symptoms and to manage their other medical conditions. Dementia is correlated with increasing age and frailty; this provides insight into how the efficacy and toxicity of medications may be altered in people with dementia. Areas covered: This review discusses the current evidence of the alterations in pharmacokinetics that can occur with aging, frailty and in people with dementia. The evidence is presented via the four primary pharmacokinetic processes (absorption, distribution, metabolism and elimination). Additionally, distribution into the brain, sex considerations and potential pharmacodynamic alterations in older people with dementia are discussed. Expert opinion: While the evidence is limited, people with dementia appear to be at a higher risk of toxicity of some medications due to altered pharmacokinetic processes and pharmacodynamics. There are a number of limitations to the research and there are still significant gaps in knowledge in this field. Proactive, ongoing review of the appropriateness of choice of medication, dose and whether or not a medication is required at all is necessary for achieving quality use of medications in people living with dementia.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.135
GPT teacher head0.473
Teacher spread0.337 · 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 designNot applicable
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

Citations80
Published2017
Admission routes1
Has abstractyes

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