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Record W4390176255 · doi:10.3390/jpm14010022

User Experiences of Pharmacogenomic Testing and Opinions among Psychiatry Patients

2023· article· en· W4390176255 on OpenAlexaff
Catherine R. Virelli, Mahbod Ebrahimi, Ayeshah G. Mohiuddin, Julia Tomasi, Amanda Lisoway, Deanna Herbert, Victoria Marshe, Sean A. Kidd, Joseph Ferenbok, James L. Kennedy

Bibliographic record

VenueJournal of Personalized Medicine · 2023
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsPharmacogenomicsMedicinePsychiatryPsychologyPharmacology

Abstract

fetched live from OpenAlex

Pharmacogenomic testing (PGx) is a tool used to guide physicians in selecting an optimal medication for clients based on their genetic profile. The objective of this qualitative study is to understand patients' experiences with PGx testing as well as their opinions regarding the clinical adoption of such tests in psychiatry. A focus group was conducted to assess the needs of clients who had experience using a PGx test. Participants were recruited from a large study on PGx testing that offered physicians an opportunity to use PGx reports to guide psychotropic prescriptions. The focus group discussions were recorded, transcribed, and coded using NVivo to identify core themes. A total of 11 people participated in the focus group. Our analysis revealed that many participants were in favour of implementing PGx testing in psychiatric practice, and all expressed important considerations for patient-centred optimization of PGx testing. The main themes captured were: education and awareness among clinicians, cost considerations, PGx results-sharing and accessibility, and prospective benefits. The results of this study suggest that patients are keen to see PGx testing in widespread clinical care, but they report important opportunities to improve knowledge mobilization of PGx testing.

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)0.000
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 score0.291
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.115
GPT teacher head0.442
Teacher spread0.327 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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