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Record W4297770594 · doi:10.18103/mra.v10i8.2934

Pharmacogenetics Implementation in Primary Care

2022· article· en· W4297770594 on OpenAlex
Martin Dawes, Bernard Esquivel

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Research Archives · 2022
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacogenetics and Drug Metabolism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPharmacogeneticsMedicineDrugMedical prescriptionIntensive care medicineSAFERWorkloadDrug responsePharmacologyComputer scienceGenotypeBiology

Abstract

fetched live from OpenAlex

Pharmacogenetics is being considered as a pre-emptive test for the National Health Service in the United Kingdom. Primary care is a key clinical service for the use of this technology due to the large number of prescriptions prescribed in this setting. Given the volume of prescribing and the prevalence of pharmacogenetic variants an average GP will be using pharmacogenetics approximately 12 times per week. The current high workload in primary care means that a time of only a minute or two at most is available for the physician to use this new information. Pharmacogenetics is only one data point in identifying drug options for a patient. In determining those options physicians also take into account drug-drug, drug-condition, drug-liver and drug-renal potential interactions. Clinical Decision Support Systems exist that use pharmacogenetic and other information to help identify safer and more effective medication options for an individual.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0170.000

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 head0.169
GPT teacher head0.558
Teacher spread0.389 · 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