Assessment of antiretroviral third agent virologic durability after initiation of first antiretroviral regimen
Bibliographic record
Abstract
Information on the virologic durability of modern antiretroviral regimens is important to clinicians. We aimed to describe virologic durability of first-line integrase strand transfer inhibitor (INSTI)-, nonnucleoside reverse transcriptase inhibitor (NNRTI)-, or protease inhibitor (PI)-based antiretroviral regimens. This was a retrospective study of antiretroviral-naïve patients that initiated first-line antiretroviral regimens with two nucleoside reverse transcriptase inhibitors and an INSTI, NNRTI, or PI between January 2006 and June 2016. The outcome was time to virologic failure, which was assessed by Kaplan–Meier survival analysis and Cox regression models. There were 780 patients (median age = 37 years [interquartile range (IQR) = 30–45], 93.3% male, 56.2% Caucasian, median HIV duration = 1.8 years [IQR = 0.4–5.4], baseline log 10 viral load [VL]=4.6 [IQR = 4.1–5.1], and baseline CD4+ cell count = 320 cells/µl [IQR = 217–440]). In total, 189/780 were on a third agent INSTI, 339/780 on a third agent NNRTI, and 252/780 on a third agent PI. Kaplan–Meier survival probability revealed longer time to virologic failure for INSTI, followed by NNRTI then PI (p < 0.001). Multivariable Cox regression revealed that being on an INSTI regimen (aHR = 0.27; 95%CI = 0.18–0.41) or NNRTI regimen (aHR = 0.64; 95%CI = 0.47–0.87) versus PI regimen, frequent VL testing (per year), (aHR = 0.64; 95%CI = 0.47–0.87), and duration of ART (aHR = 0.22; 95%CI = 0.17–0.30) (years) were inversely associated with time to virologic failure, and log 10 of baseline VL (aHR = 1.94; 95%CI = 1.58–2.39 per log 10 ) increased risk. Virologic failure was delayed and virologic durability prolonged for INSTI- compared to NNRTI- and PI-based regimens, supporting current antiretroviral therapy guidelines.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".