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Record W4322623465 · doi:10.1515/dmpt-2022-0194

A review of pharmacogenetic studies in the Bangladeshi population

2023· review· en· W4322623465 on OpenAlexaff
Md Shaki Mostaid, Md. Abdul Aziz, Jeba Atkia Maisha, Mohammad Safiqul Islam, Abdullah Al Maruf

Bibliographic record

VenueDrug Metabolism and Personalized Therapy · 2023
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPharmacogeneticsPopulationMedicineBiologyGeneticsEnvironmental healthGenotype

Abstract

fetched live from OpenAlex

Abstract Pharmacogenetics (PGx)-guided prescribing is an evidence-based precision medicine strategy. Although the past two decades have reported significant advancements in both the quality and quantity of PGx research studies, they are seldom done in developing countries like Bangladesh. This review identified and summarized PGx studies conducted in the Bangladeshi population by searching PubMed and Google Scholar. Additionally, a quality evaluation of the identified studies was also carried out. Eleven PGx studies were identified that looked at the effects of genetic variants on blood thinners ( CYP2C9 , VKORC1 , and ITGB3 ), cancer drugs ( TPMT , MTHFR , DPYD , ERCC1 , GSTP1 , XPC , XRCC1 , TP53 , XPD , and ABCC4 ), statins ( COQ2 , CYP2D6 , and CYP3A5 ), and prednisolone ( ABCB1 , CYP3A5 , and NR3C1 ) in the Bangladeshi population. Most studies were of low to moderate quality. Although the identified studies demonstrated the potential for PGx testing, the limited PGx literature in the Bangladeshi population poses a significant challenge in the widespread implementation of PGx testing in Bangladesh.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0070.009
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.420
GPT teacher head0.555
Teacher spread0.135 · 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 designSystematic review
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

Citations2
Published2023
Admission routes1
Has abstractyes

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