Efavirenz and CYP2B6 Polymorphism: Implications for Drug Toxicity and Resistance
Bibliographic record
Abstract
The brief history of pharmacogenetics has been marked by a high level of public as well as professional interest, reflecting in many respects the great promise offered by genomic approaches to biology, clinical medicine, and pharmacology over the past ∼15 years. This early interest in personalized medicine was fueled by reviews [1, 2] in which it was predicted that drug prescribing could be undertaken with greater certainty and objectivity after elucidation of genetic traits determining variable drug disposition between individuals. The noted geneticist Allen Roses raised the ghost of Sir William Osler in his contemplation of the quote “If it were not for the great variability among individuals, medicine might as well be a science and not an art” (p.857) [2]. Roses' response, made just over one hundred years later (in 2000), hinted that the end was in sight for the art of medicine, offering that “...we are on the verge of being able to identify inherited differences between individuals which can predict each patient's response to a medicine. This ability will have far-reaching benefits in the discovery, development and delivery of medicines” (p. 857) [2].
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.021 | 0.010 |
| 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".