Bictegravir/emtricitabine/tenofovir alafenamide in patients with genotypic <scp>NRTI</scp> resistance
Bibliographic record
Abstract
BACKGROUND: Bictegravir/emtricitabine/tenofovir alafenamide (B/F/TAF) is approved for treatment of HIV without known resistance to its components. Several studies have demonstrated efficacy of B/F/TAF in patients with nucleoside reverse transcriptase inhibitor (NRTI) resistance-associated mutations (RAMs), mainly identified by proviral DNA testing, but data on the efficacy of B/F/TAF in patients with NRTI RAMs identified in viraemic plasma are limited. METHODS: We used a retrospective analysis of patients receiving B/F/TAF identified by searching electronic health records with eligibility confirmed by review of individual patient records. Patients included were ≥ 18 years, had 2019 International Antiviral Socitey-USA (IAS-USA) major RAMs affecting NRTIs detected in viraemic plasma prior to starting B/F/TAF and one or more HIV viral load (VL) after starting B/F/TAF. RESULTS: In all, 50 patients met the study criteria: mean age of 54 years, mean proximal CD4 count of 609 cells/μL, 64% male. A total of 46 were virologically suppressed (< 200 copies/mL) when B/F/TAF was initiated, two were treatment-naïve, one stopped prior antiretroviral therapy (ART) and one had a VL of 961 HIV-1 RNA copies/mL on ART. Twenty-nine had one NRTI RAM (24 were M184V/I), nine had two NRTI RAMs, three had three NRTI RAMs, four had four NRTI RAMs, two had five NRTI RAMs, one had six NRTI RAMs, one had seven RAMs and one had eight NRTI RAMs. At the last VL on B/F/TAF, a mean of 18.6 months after starting B/F/TAF, 49 out of 50 had VL < 100 copies/mL and one had a VL of 208 copies/mL at 11 months but only filled 5 months of B/F/TAF. CONCLUSIONS: B/F/TAF was effective in maintaining HIV VL suppression in patients with previously documented NRTI RAMs without integrase resistance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".