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Record W2143110843 · doi:10.1503/jpn.100059

Pharmacogenetics of antidepressant response

2011· review· en· W2143110843 on OpenAlexvenueno aff
Stefano Porcelli, Antonio Drago, Chiara Fabbri, Sara Gibiino, Raffaella Calati, Alessandro Serretti

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2011
Typereview
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPharmacogeneticsAntidepressantMedicinePharmacologyPsychologyPsychiatryBiologyGeneticsGenotypeGeneAnxiety

Abstract

fetched live from OpenAlex

Personalized medicine - the adaptation of therapies based on an individual's genetic and molecular profile - is one of the most promising aspects of modern medicine. The identification of the relation between genotype and drug response, including both the therapeutic effect and side effect profile, is expected to deeply affect medical practice. In this paper, we review the current knowledge about the genes related to antidepressant treatment response and provide methodologic proposals for future studies. We have mainly focused on genes associated with pharmacodynamics, for which a list of promising genes has been identified despite some inconsistency across studies. We have also synthesized the main results for pharmacokinetic genes, although so far they seem less relevant than those for pharmaco dynamic genes. We discuss possible reasons for these inconsistent findings and propose new study designs.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.084
GPT teacher head0.355
Teacher spread0.271 · 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 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

Citations179
Published2011
Admission routes1
Has abstractyes

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