Use of metabolic drugs and fish oil in HIV‐positive patients with metabolic complications and associations with dyslipidaemia and treatment targets
Bibliographic record
Abstract
BACKGROUND: Highly active antiretroviral therapy (HAART) with protease inhibitors (PI) is successful in suppressing viral replication, but may lead to a range of metabolic abnormalities associated with cardiovascular disease (CVD). OBJECTIVES: The first objective of the study was to compare baseline demographic and clinical characteristics between PI users and non-PI users referred to a specialized metabolic clinic during 1999-2003. The second objective was to assess the associations of prescription drugs and fish oil with dyslipidaemia and to determine whether or not patients achieved treatment targets during 6 months of treatment. METHODS: A retrospective analysis was performed using two sets of charts based on standardized forms with entries for personal data, drug treatment and clinical history. Anonymous linkage with the British Columbia HIV/AIDS Drug Treatment Program and the hospital laboratory was performed to gather information about HAART prescriptions and blood work. RESULTS: In total, 237 patients were included in the study. There were few differences in any demographic or clinical factors between PI users and non-PI users. Compared with controls not taking lipid-lowering drugs or fish oil (n=48), statins appeared to be the only agent that was significantly associated with a reduced total cholesterol concentration (-15.6%; P=0.009). Fibrate treatment was associated with the largest reduction of triglyceride concentration (-37.4%; P=0.012), closely followed by fish oil (n=18;-32%; P=0.027). Six-month treatment success rates ranged between 17 and 43% of patients for total cholesterol (<5.2 mmol/L) and between 15 and 44% of patients for triglycerides (<2.3 mmol/L). CONCLUSIONS: Despite the apparent lowering of blood lipids with drug and fish oil treatments, a majority of patients in these treatment groups (56.5-83.3%) still had elevated concentrations after 6 months.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".