Nucleoside and Nucleotide Analogue Reverse Transcriptase Inhibitors: A Clinical Review of Antiretroviral Resistance
Bibliographic record
Abstract
Although advances in highly active antiretroviral therapy (HAART) have made long-term suppression of HIV an achievable goal of therapy, a substantial proportion of first-line regimens will eventually fail. Successful longterm treatment requires consideration of downstream treatment options at the time of initiating or changing regimens. An understanding of the patterns and interactions of resistance mutations, and the appropriate use of genotypic and phenotypic testing is an important component of successful drug sequencing. Resistance to multiple nucleoside reverse transcriptase inhibitors (NRTIs) may result from several genotypically distinct pathways, including the Q151M (151 complex), the 69 insertion complex, two distinct thymidine analogue mutational pathways and the K65R mutation. Knowledge of the clinical implications of these and other resistance pathways, as well as the antagonism or synergy between mutations, helps guide individualized treatment choices from initial therapy in the treatment-naive patient to salvage therapy in the highly treatment-experienced individual. The development of effective sequencing strategies will depend upon the continued understanding of drug resistance mutation patterns and their associations with specific HAART combinations. This review summarizes research advances that further the understanding of nucleoside and nucleotide analogue resistance mutations, and their interplay.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".