The Prevalence of Transmitted Resistance to First-Generation Non-Nucleoside Reverse Transcriptase Inhibitors and Its Potential Economic Impact in HIV-Infected Patients
Bibliographic record
Abstract
Non-nucleoside reverse transcriptase inhibitor (NNRTI)-based highly active antiretroviral therapy (HAART) including efavirenz is recommended as a 1(st)-line treatment choice in international HIV guidelines, and it is one of the most common components of initial therapy. Resistance to 1(st)-generation NNRTIs is found among treated and untreated HIV-infected individuals creating a subpopulation of HIV-infected individuals in whom efavirenz is not fully effective. This analysis reviewed published articles and conference abstracts to examine the prevalence of 1(st)-generation NNRTI resistance in Europe, the United States (US), and Canada and to identify published evidence of the economic consequences of resistance. The reported prevalence of NNRTI resistance was generally higher in US/Canada than in Europe and increased in both regions from their introduction in the late 1990s until the early 2000s. The most recent time-based trends suggest that NNRTI-resistance prevalence may be stable or decreasing. These estimates of resistance may be understated as resistance estimates using ultra-sensitive genotypic testing methods, which identify low-frequency mutations undetected by standard testing methods, showed increased prevalence of resistance by more than two-fold. No studies were identified that explicitly investigated the costs of drug resistance. Rather, most studies reported costs of treatment change, failure, or disease progression. Among those studies, annual HIV medical costs of those infected with HIV increased 1) as CD4 cells decreased, driven in part by hospitalization at lower CD4 cell counts; 2) for treatment changes, and 3) for each virologic failure. The possible erosion of efficacy or of therapy choices through resistance transmission or selection, even when present with low frequency, may become a barrier to the use of 1(st)-generation NNRTIs and the increased costs associated with regimen failure and disease progression underlie the importance of identification of treatment resistance to ensure optimal initial therapy choice and regimen succession.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".