Serum Triglycerides, HIV Infection, and Highly Active Antiretroviral Therapy, Aquitaine Cohort, France, 1996 to 1998
Bibliographic record
Abstract
The aim of this study was to identify factors associated with serum triglyceride (TG) evolution in HIV-1-infected patients when highly active antiretroviral treatment (HAART) with or without protease inhibitors (PI) was introduced. Among 3191 patients of the Aquitaine Cohort (multirisk, both genders, multiple treatment patterns) observed during 1996 through 1998, 1429 had at least two measurements of TG, viral load, and CD4 cell count. Median follow-up was 21 months (interquartile range [IQR], 11-26) and median number of TG measures was 6 (IQR, 3-10). Median TG at baseline was 1.32 mmol/L (IQR, 0.91-2.05) and increased significantly over time (+2.5% for 100 days; 95% confidence interval [CI], 1.9-3.1). Longitudinal analysis of variations of TG was performed using mixed models. In crude analysis, baseline TG was higher in men, in those aged over 36 years, and in homosexuals. The following time-dependent variables were associated with an increase of TG: body weight increasing to >65 kg, diagnosis of AIDS, CD4 cell count falling to <50 cells/mm3, viral load falling to <500 cp/ml, and introduction of nucleoside analogues and PIs. In multivariate analysis, age >36 years (change of +17% of the TG level; 95% CI, 11-24), homosexuals (+13%; 95% CI, 4-23), AIDS stage (+12%; 95% CI, 5-19), weight >65 kg (+7%; 95% CI, 2-12) and PI (+21%; 95% CI, 17-27) remained significant. Factors identified before the availability of PI remain important but HAART with PI is a new major contributing factor to increased TG levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".