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Record W2493281570 · doi:10.2217/pgs.16.26

Pharmacogenetics of Tardive Dyskinesia: An Updated Review of the Literature

2016· review· en· W2493281570 on OpenAlexafffund
Rachel Lanning, Clement C. Zai, Daniel J. Müller

Bibliographic record

VenuePharmacogenomics · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsTardive dyskinesiaPharmacogeneticsMedicineDyskinesiaPharmacologyInternal medicinePsychiatryGeneticsBiologySchizophrenia (object-oriented programming)GenotypeDisease

Abstract

fetched live from OpenAlex

Tardive dyskinesia (TD) is a serious and potentially irreversible side effect of long-term exposure to antipsychotic medication characterized by involuntary trunk, limb and orofacial muscle movements. Various mechanisms have been proposed for the etiopathophysiology of antipsychotic-induced TD in schizophrenia patients with genetic factors playing a prominent role. Earlier association studies have focused on polymorphisms in CYP2D6, dopamine-, serotonin-, GABA- and glutamate genes. This review highlights recent advances in the genetic investigation of TD. Recent promising findings were obtained with the HSPG2, DPP6, MTNR1A, SLC18A2, PIP5K2A and CNR1 genes. More research, including collection of well-characterized samples, enhancement of genome-wide strategies, gene-gene interaction and epigenetic analyses, is needed before genetic tests with clinical utility can be made available for TD.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.336
Teacher spread0.318 · 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
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

Citations47
Published2016
Admission routes2
Has abstractyes

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