Impact of Baseline Virologic, Immunologic, and Demographic Characteristics on Virologic Responses in the Gemini Study
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To determine the impact of baseline viral load (VL) and CD4+ cell count, race/ethnicity, and gender on response in a post hoc analysis of the Gemini study. METHODS: In this 48-week study, treatment-naïve, HIV-infected participants received as initial therapy twice-daily saquinavir/ritonavir (SQV/r) 1000/100 mg (n=167) or lopinavir/ritonavir (LPV/r) 400/100 mg (n=170), each with emtricitabine 200 mg/tenofovir 300 mg daily. The proportion of participants achieving HIV RNA<50 copies/mL (primary endpoint) and median change from baseline in CD4+ cell count were compared by baseline VL (>100,000 vs ≤ 100,000 copies/ mL) and CD4+ cell count (>100 vs ≤ 100 cells/µL). The impact of baseline and demographic variables on virologic response was assessed by logistic regression analysis. RESULTS: Responses were similar between arms (SQV/r vs LPV/r) with or without stratification. In a pooled analysis of SQV/r and LPV/r arms, CD4+ cell count >100 cells/µL (odds ratio [OR], 1.628;P = .0416), non-Thai/non-Black versus Black race (OR, 1.518;P = .0023), and non-Thai/non-Black versus Thai (OR, 0.467;P = .0046) were significant predictors of virologic response. CONCLUSIONS: Treatment groups had similar efficacy. Baseline CD4+ cell count and race/ethnicity were independent predictors of virologic response, whereas baseline VL and gender were not.
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.
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 | 0.028 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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