Dolutegravir efficacy at 48 weeks in key subgroups of treatment-naive HIV-infected individuals in three randomized trials
Bibliographic record
Abstract
OBJECTIVES: Dolutegravir (DTG) has been studied in three trials in HIV treatment-naive participants, showing noninferiority compared with raltegravir (RAL), and superiority compared with efavirenz and ritonavir-boosted darunavir. We explored factors that predicted treatment success, the consistency of observed treatment differences across subgroups and the impact of NRTI backbone on treatment outcome. DESIGN: Retrospective exploratory analyses of data from three large, randomized, international comparative trials: SPRING-2, SINGLE, and FLAMINGO. METHODS: We examined the efficacy of DTG in HIV-infected participants with respect to relevant demographic and HIV-1-related baseline characteristics using the primary efficacy endpoint from the studies (FDA snapshot) and secondary endpoints that examine specific elements of treatment response. Regression models were used to analyze pooled data from all three studies. RESULTS: Snapshot response was affected by age, hepatitis co-infection, HIV risk factor, baseline CD4⁺ cell count, and HIV-1 RNA and by third agent. Differences between DTG and other third agents were generally consistent across these subgroups. There was no evidence of a difference in snapshot response between abacavir/lamivudine (ABC/3TC) and tenofovir/emtricitabine (TDF/FTC) overall [ABC/3TC 86%, TDF/FTC 85%, difference 1.1%, confidence interval (CI) -1.8, 4.0 percentage points, P = 0.61] or at high viral loads (difference -2.5, 95% CI -8.9, 3.8 percentage points, P = 0.42). CONCLUSIONS: DTG is a once-daily, unboosted integrase inhibitor that is effective in combination with either ABC/3TC or TDF/FTC for first-line antiretroviral therapy in HIV-positive individuals with a variety of baseline characteristics.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".