Effects of drug resistance on viral load in patients failing antiretroviral therapy
Bibliographic record
Abstract
Previous studies on patients who develop drug resistant HIV-1 variants have shown that continued use of failing regimens might provide clinical benefit. However, the effect of long-term exposure to drug resistant variants may lead to emergence of compensatory mutations that may jeopardize this effect. In this study, we assess associations among type and number of drug resistant mutations, viral load and disease progression in patients with long-term follow up. Patients with genotypic testing performed at the time of treatment failure were enrolled. Comparison of viral load and CD4 cell count between different resistance groups was performed using analysis of variance. Multiple linear regression analysis was performed to assess the simultaneous effects of the presence of particular mutations and their accumulation on viral load. Data from 475 patients who were followed for a median of 43 months from October 1999 to July 2005 were studied. A "V shape" relationship was observed between the number of mutations and viral load. Specifically, in patients harboring up to five mutations, viral load was reduced by 0.8 log/copies when compared to wild-type variants. However, with more than six mutations viral load progressively increased. Certain reverse transcriptase mutations such as M184V/I, K70R, V108I, and protease mutations such as L33FIV, M84V, and M36I were associated with reduced viral load. Together, these findings suggest that long-term maintenance of a sub-optimal antiretroviral regimen may have deleterious consequences for the patient.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".