Impact of Baseline Virologic, Immunologic, and Demographic Characteristics on Virologic Responses in the Gemini Study
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.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".