Bibliographic record
Abstract
Polymorphisms exhibited by drug-metabolizing enzymes are well known and have been investigated for many years. Recently, the exploding field of pharmacogenetics has focused not only on the characterization of enzymes responsible for drug biotransformation but also, on describing the sources of variability in enzyme activity. While initial observations and studies focused on populations of Caucasian origin, reports for other populations followed. The incidence of a poor or slow metabolizer phenotype for a given enzyme caused by allelic variants may vary significantly between populations. The question arises as to whether a prediction of the phenotype (i.e. distribution and/or enzyme activity) can be accurately ascertained from genotype information gathered in a related population. This is exemplified by NAD(P):quinone oxidoreductase (NQO1) investigated in Canadian Native Indian (CNI), Inuit and Chinese populations and the cytochromes P4502C19 and 2D6. While the two North American Native populations are genetically distinct, they are both descendants from northern Asia. Consequently, one might suspect that on a pharmacogenetic basis, CNI and Inuit would be more comparable to Chinese as opposed to Caucasian populations. This is certainly not the case as demonstrated for all three enzymes. Also, for a reliable phenotype prediction, one needs to pay attention to ethnic "mixing" which occurs between certain populations. Ethnic diversity constitutes both a challenge and an opportunity to prudently apply pharmacogenetics so that variability in both drug disposition and effect may be better understood.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".