Bibliographic record
Abstract
Until about 50 years ago, the altering of a normal drug effect by a genetic deficiency was only rarely observed. Here, my discovery of the genetic variant of butyrylcholinesterase affecting succinylcholine action is described in some detail. Such discoveries led to the combination of the two older sciences, genetics and pharmacology, thereby forming pharmacogenetics. After the discovery of similar examples in the years that followed, pharmacogenetics expanded on the basis of two discoveries. First, the common occurrence of interethnic differences in drug response and, secondly, the fact that most pharmacological differences were multigenic. New methodologies brought a transition to pharmacogenomics; this included detection of clinically important genetic variants and has uncovered potentially new drug targets. The arrival of personalised medicine--where a patient's genes determine the choice of drug to be administered--can be hoped to gradually improve drug safety and efficacy. Efforts to reach this level of perfection are, however, dogged by 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 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 | 0.023 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".