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Record W1958070321 · doi:10.1002/clc.22200

Pharmacogenetics in Cardiovascular Disease: The Challenge of Moving From Promise to Realization

2013· article· en· W1958070321 on OpenAlex
Philip Joseph, Guillaume Paré, Stephanie Ross, Robert Roberts, Sonia S. Anand

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClinical Cardiology · 2013
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of OttawaHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicinePharmacogeneticsPrecision medicinePersonalized medicinePharmacogenomicsIntensive care medicineDiseasePharmacodynamicsClinical pharmacologyPharmacologyBioinformaticsInternal medicinePharmacokineticsGenotypePathology

Abstract

fetched live from OpenAlex

Pharmacogenetics in cardiovascular medicine brings the potential for personalized therapeutic strategies that improve efficacy and reduce harm. Studies evaluating the impact of genetic variation on pharmacologic effects have been undertaken for most major cardiovascular drugs, including antithrombotic agents, β-adrenergic receptor blockers, statins, and angiotensin-converting enzyme inhibitors. Across these drug classes, many polymorphisms associated with pharmacodynamic, pharmacokinetic, or surrogate outcomes have been identified. However, their impact on clinical outcomes and their ability to improve clinical practice remains unclear. This review will examine the current clinical evidence supporting pharmacogenetic testing in cardiovascular medicine, provide clinical guidance based on the current evidence, and identify further steps needed to determine the utility of pharmacogenetics in cardiovascular care.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.191
GPT teacher head0.464
Teacher spread0.273 · 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