Time to Viremia for Patients Taking their First Antiretroviral Regimen and the Subsequent Resistance Profiles
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it