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Record W2051366695 · doi:10.1159/000064194

Ethical, Social and Legal Implications of Pharmacogenomics: A Critical Review

2001· review· en· W2051366695 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePublic Health Genomics · 2001
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsnot available
FundersHealth Technology Assessment international
KeywordsPharmacogenomicsBioethicsMultidisciplinary approachEngineering ethicsMedicinePsychologyPolitical scienceSociologySocial sciencePharmacologyLawEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: My aim was to examine the ethical, social and legal implications of pharmacogenomics. METHODS: I performed a critical review of the literature. The primary focal point is the bioethical principle discussed. The second outcome measure is the perspective of the discussion. RESULTS: This review documents that the pharmacogenomics issues of concern are comparable to issues concerning other genetic developments in general. However, two main issues are particular to the case of pharmacogenomics. Firstly, this review reveals that society, industry, groups and individuals appreciate the prospect of pharmacogenomics very differently. Secondly, there is a lack of research into the post-marketing implications of pharmacogenomics. CONCLUSION: An extensive focus on the ethical, social and legal implications of pharmacogenomics, in terms of both pre- as well as post-marketing issues, is essential. Also, a multidisciplinary approach which includes individual and group opinions in an upfront manner in the research and development process is essential. Otherwise, there is a substantial risk that the positive prospects of pharmacogenomics will not survive due to fear and a lack of acceptance and understanding on the part of the general public.

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.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
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 head0.537
GPT teacher head0.595
Teacher spread0.059 · 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