Bibliographic record
Abstract
PURPOSE OF REVIEW: Although the past 20 years have seen many advances in HIV pharmacotherapy, host variability has only been more recently recognized as an important driver of both treatment response and toxicity. This review will focus on the importance of variability in drug metabolizing enzymes and the potential research and clinical applications of these findings in HIV care. RECENT FINDINGS: The treatment of HIV is complex and involves chronic treatment with multiple drugs and drug classes. Recent research has been important in uncovering genetic differences in drug metabolizing and drug transporter genes, which underpin dose-dependent toxicity and efficacy, but also in genes defining differences in immune response and human leukocyte antigen genes, which restrict processes that are less dependent on the drug dose. SUMMARY: The advancement of pharmacogenetics will continue to further our knowledge of disease-drug interactions and pathogenesis. Ultimately, the goals of pharmacogenetics and pharmacogenomics in HIV medicine will be the development of the right drugs for the right patient or population. The strong association between HLA-B5701 and abacavir hypersensitivity reaction shows promise for a genetic screening test to be feasibly incorporated into clinical practice. The clinical applicability of many findings demonstrating associations between single nucleotide polymorphisms, particularly in drug metabolism and transporter genes and drug efficacy and toxicity, are currently uncertain.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".