Changes in weight and BMI with first-line doravirine-based therapy
Bibliographic record
Abstract
OBJECTIVE: To evaluate changes in weight and BMI in adults with HIV-1 at 1 and 2 years after starting an antiretroviral regimen that included doravirine, ritonavir-boosted darunavir, or efavirenz. DESIGN: Post-hoc analysis of pooled data from three randomized controlled trials. METHODS: We evaluated weight change from baseline, weight gain at least 10%, and increase in BMI after 48 and 96 weeks of treatment with doravirine, ritonavir-boosted darunavir, or efavirenz-based regimens. Risk factors for weight gain and metabolic outcomes associated with weight gain were also examined. RESULTS: Mean (and median) weight changes were similar for doravirine [1.7 (1.0) kg] and ritonavir-boosted darunavir [1.4 (0.6) kg] and were lower for efavirenz [0.6 (0.0) kg] at week 48 but were similar across all treatment groups at week 96 [2.4 (1.5), 1.8 (0.7), and 1.6 (1.0) kg, respectively]. No significant differences between treatment groups were found in the proportion of participants with at least 10% weight gain or the proportion with BMI class increase at either time point. Low CD4 T-cell count and high HIV-1 RNA at baseline were associated with at least 10% weight gain and BMI class increase at both timepoints, but treatment group, age, sex, and race were not. CONCLUSION: Weight gains over 96 weeks were low in all treatment groups and were similar to the average yearly change in adults without HIV-1. Significant weight gain and BMI class increase were similar across the treatment groups and were predicted by low baseline CD4 T-cell count and high baseline HIV-1 RNA.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".