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Record W2157230121 · doi:10.2217/thy.10.3

Pharmacogenetics of antipsychotic treatment response and side effects

2010· article· en· W2157230121 on OpenAlexafffund
Bronwyn Mackenzie, Renan P. Souza, Olga Likhodi, AK Tiwari, Clement C. Zai, Jessica E. Sturgess, Müller Dj

Bibliographic record

VenueTherapy · 2010
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCentre for Addiction and Mental Health
FundersCanadian Institutes of Health ResearchU.S. Public Health Service
KeywordsMedicineAntipsychoticPharmacogeneticsTardive dyskinesiaSide effect (computer science)Antipsychotic drugPsychiatryDrugDyskinesiaSchizophrenia (object-oriented programming)PharmacologyInternal medicineGenotypeGeneGenetics

Abstract

fetched live from OpenAlex

Antipsychotic drugs are particularly interesting in pharmacogenetic studies as they are associated with a large interindividual variability in terms of response and side effects and, therefore, frequently need to be discontinued, requiring switches to other antipsychotics. Any information that allows the prediction of outcome to a given antipsychotic in a particular patient will, therefore, be of great help for the clinician to minimize time and find the right drug for the right patient, thus optimizing response and minimizing side effects. This will also have a substantial impact on compliance and doctor-patient relationships. Moreover, antipsychotic drug treatments are often required for life-long treatment and are also frequently prescribed to the more 'vulnerable' populations: children, adolescents and the elderly. This article focuses on some important studies performed with candidate gene variants associated with antipsychotic response. In addition, important findings in pharmacogenetic studies of antipsychotic-induced side effects will be briefly summarized, such as antipsychotic treatment induced tardive dyskinesia and weight gain.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.353
Teacher spread0.330 · 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

Citations14
Published2010
Admission routes2
Has abstractyes

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