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Record W2161620765 · doi:10.1002/cpt.147

Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for <i>CYP2D6</i> and <i>CYP2C19</i> Genotypes and Dosing of Selective Serotonin Reuptake Inhibitors

2015· article· en· W2161620765 on OpenAlexaff
J K Hicks, JR Bishop, Katrin Sangkuhl, Müller Dj, Yuan Ji, SG Leckband, JS Leeder, RL Graham, DL Chiulli, Adrián LLerena, TC Skaar, Stuart A. Scott, Julia Stingl, Teri E. Klein, KE Caudle, Andrea Gaedigk

Bibliographic record

VenueClinical Pharmacology & Therapeutics · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute of General Medical SciencesNational Institute of Mental HealthNational Heart, Lung, and Blood Institute
KeywordsFluvoxamineEscitalopramCitalopramCYP2C19ParoxetinePharmacogeneticsSertralineDosingMedicinePharmacologyCYP2D6FluoxetinePsychiatryAnxietySerotoninInternal medicineGenotypeAntidepressantBiology

Abstract

fetched live from OpenAlex

Selective serotonin reuptake inhibitors (SSRIs) are primary treatment options for major depressive and anxiety disorders. CYP2D6 and CYP2C19 polymorphisms can influence the metabolism of SSRIs, thereby affecting drug efficacy and safety. We summarize evidence from the published literature supporting these associations and provide dosing recommendations for fluvoxamine, paroxetine, citalopram, escitalopram, and sertraline based on CYP2D6 and/or CYP2C19 genotype (updates at www.pharmgkb.org).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.060
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0040.002
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0110.007

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 head0.295
GPT teacher head0.567
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations975
Published2015
Admission routes1
Has abstractyes

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