Time to Viremia for Patients Taking their First Antiretroviral Regimen and the Subsequent Resistance Profiles
Bibliographic record
Abstract
BACKGROUND: The resistance profiles for patients on first-line antiretroviral therapy (ART) regimens after viremia have not been well studied in community clinic settings in the modern treatment era. OBJECTIVE: To determine time to viremia and the ART resistance profiles of viremic patients. METHODS: HIV-positive patients aged ≥16 years initiating a three-drug regimen were retrospectively identified from 01/01/06 to 12/31/12. The regimens were a backbone of two nucleoside reverse transcriptase inhibitors (NRTIs) and a third agent: a protease inhibitor (PI), non-nucleoside reverse transcriptase inhibitor (NNRTI), or an integrase inhibitor (II). Time to viremia was compared using a proportional hazards model, adjusting for demographic and clinical factors. Resistance profiles were described in those with baseline and follow-up genotypes. RESULTS: For 653 patients, distribution of third-agent use and viremia was: 244 (37%) on PIs with 80 viremia, 364 (56%) on NNRTIs with 84 viremia, and 45 (7%) on II with 11 viremia. Only for NNRTIs, time to viremia was longer than PIs (p = 0.04) for patients with a CD4 count ≥200 cells/mm(3). Of the 175 with viremia, 143 (82%) had baseline and 37 (21%) had follow-up genotype. Upon viremia, emerging ART resistance was rare. One new NNRTI (Y181C) mutation was identified and three patients taking PI-based regimens developed NRTI mutations (M184 V, M184I, and T215Y). CONCLUSIONS: Time to viremia for NNRTIs was longer than PIs. With viremia, ART resistance rarely developed without PI or II mutations, but with a few NRTI mutations in those taking PI-based regimens, and NNRTI mutations in those taking NNRTI-based regimens.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.004 | 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".