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Genetic testing for CYP2D6 and CYP2C19 suggests improved outcome for antidepressant and antipsychotic medication

2018· article· en· W2792305076 on OpenAlexafffund
Lucas M. Walden, Eva J. Brandl, Arun K. Tiwari, Sheraz Cheema, Natalie Freeman, Nicole Braganza, James L. Kennedy, Daniel J. Müller

Bibliographic record

VenuePsychiatry Research · 2018
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Institute of Mental HealthCanadian Institutes of Health ResearchCharité – Universitätsmedizin BerlinU.S. Food and Drug AdministrationNational Institutes of HealthBerlin Institute of HealthCentre for Addiction and Mental Health Foundation
KeywordsCYP2C19PharmacogeneticsCYP2D6MedicineGenotypingMedical prescriptionAntipsychoticAntidepressantPsychiatryPharmacogenomicsClinical significanceInternal medicineClinical psychologyPharmacologyGenotypeSchizophrenia (object-oriented programming)Genetics

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.305
GPT teacher head0.551
Teacher spread0.246 · 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 designObservational
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

Citations46
Published2018
Admission routes2
Has abstractno

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