The effect of low‐dose ritonavir monotherapy on fasting serum lipid concentrations
Bibliographic record
Abstract
OBJECTIVES: Ritonavir (RTV) at doses of 400 mg twice a day (bid) or higher adversely affects serum lipids. However, the effect of RTV 100 mg bid on serum lipids is unknown. We conducted a study to evaluate the effect of RTV 100 mg bid on fasting serum lipid profiles in HIV-negative healthy volunteers. METHODS: Ritonavir 100 mg bid was administered for 14 days to 20 healthy HIV-seronegative adults with normal serum lipids. After a 7-day washout, lopinavir/ritonavir (LPV/RTV) 400/100 mg bid was administered for 14 days. Fasting serum lipid parameters were measured twice at baseline, after 14 days of RTV, and after 14 days of LPV/RTV, and comparisons were made at each time-point for levels of total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, the total/HDL cholesterol ratio and triglycerides. RESULTS: After 14 days of RTV 100 mg bid, total cholesterol level increased by 10.2% (P<0.001), LDL cholesterol level increased by 16.2% (P<0.001), triglyceride levels increased by 26.5% (P<0.001), HDL cholesterol level decreased by 5.4% (P<0.01) and the total/HDL cholesterol ratio increased by 17.3% (P<0.001). The addition of LPV 400 mg bid to RTV 100 mg bid resulted in no significant further changes in LDL cholesterol or triglyceride level or total/HDL cholesterol ratio, but there were significant increases in both total cholesterol (8.0% increase; P=0.007) and HDL cholesterol levels (6.7% increase; P=0.008). CONCLUSIONS: Ritonavir dosed at 100 mg bid significantly increased the concentration of total cholesterol, LDL cholesterol, total/HDL cholesterol ratio and triglycerides and reduced HDL cholesterol concentration. The addition of LPV 400 mg bid to RTV 100 mg bid further increased both total and HDL cholesterol levels without affecting the total/HDL ratio.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".