Connection Domain Mutations in Treatment-Experienced Patients in the OPTIMA Trial
Bibliographic record
Abstract
OBJECTIVES: To determine the frequency of mutations in the connection domain (CD) of HIV reverse transcriptase in treatment-experienced patients in the Options in Management with Antiretrovirals trial, their impact on susceptibility to antiretroviral (ARV) drugs, and their impact on virologic outcomes. METHODS: Baseline plasma ARV genotypes and inferred resistance phenotypes were obtained. Frequencies of E312Q, Y318F, G333D, G333E, G335C, G335D, N348I, A360I, A360V, V365I, A371V, A376S, and E399G were compared with a treatment-naive population. The association of CD mutations with inferred IC50 fold changes to nucleos(t)ide reverse transcriptase inhibitors was evaluated. Univariate and multivariate analyses examined the association of CD mutations with a >1 log10 per milliliter decrease in HIV viral load after 24 weeks on a new ARV regimen. RESULTS: Higher CD mutation rates were seen in Options in Management with Antiretrovirals patients (n = 345) compared with a treatment-naive population. CD mutations were associated with increased inferred IC50 fold changes to abacavir, stavudine, tenofovir, and zidovudine. On univariate analysis, A371V was associated with lack of virologic response, as was having any CD mutation on multivariate analysis. CONCLUSIONS: CD mutations are frequent in treatment-experienced populations. They are associated with reduced susceptibility to some nucleos(t)ide reverse transcriptase inhibitors and with a diminished response to ARV therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".