Bibliographic record
Abstract
The traditional concern of pharmacogenetics was Mendelian (monogenic) variation, which visibly affected some drug responses. Pharmacogenetics was broadened by the observation that multifactorial genetic influences, in conjunction with environmental factors, usually determine drug responses. Variability of gene expression, a new theme of the science of genetics, also affects pharmacogenetics; for example, enhanced enzyme activity does not necessarily indicate a mutation, but may be the consequence of a drug-induced enhancement of gene expression. Methodological advances permit the conversion of pharmacogenetics into the broad practice of pharmacogenomics; this improves the possibility of identifying genetic causes of common diseases, which means establishing new drug targets, thereby stimulating the search for new drugs. While the main medical effect of pharmacogenetics was an improvement of drug safety, pharmacogenomics is hoped to improve drug efficacy. On the way to personalized medicine, we may stepwise improve the chances of choosing the right drug for a patient by categorizing patients into genetically definable classes that have similar drug effects (as, for example, human races, or any population group carrying a particular set of genes). It is wise to expect that, even after we have reached the goal to establish personalized medicine, we will not have eliminated all uncertainties.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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; both teacher heads agree on what is shown here.
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".