Bibliographic record
Abstract
Pharmacogenomics is an extension of pharmacogenetics, a science described here in terms of five stages of development: 1) some clinical observations predicted genetic alterations of drug response; 2) additional case discoveries led to the term "pharmacogenetics," a concept broadened by 3) many systemic case studies, and the realization of its wide applicability; 4) came the recognition of systematic pharmacogenetic differences between human populations. Then it became clear that 5) most human drug-response differences were multifactorial, caused by many genetic alterations plus environmental factors. The recognition of these complexities, and the advance of genetics into genomics led to the broader science of pharmacogenomics. This led to plans to create "personalized medicine," that is, making drug use more effective and safer by giving drugs that fit a person's genes. Much of the science of genetics, dealing with gene structure, was changed by the realization that gene expression and thereby gene function was variable; this leads to systematic studies of drug action on genes, reversing the traditional studies of genes affecting drug action. Finally, the realization that gene-protein variations contribute to most common diseases leads to efforts of creating new drugs that act on these variants.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".