Association between lipodystrophy and length of exposure to ARTs in adult HIV-1 infected patients in Montreal
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to establish the prevalence of lipodystrophy and its association to cumulative exposure to antiretroviral drugs. METHOD: We conducted a cross sectional study in all HIV- infected patients attending the HIV clinic in the Centre hospitalier universitaire de Montréal (CHUM) with DEXA scan. Lipodystrophy was defined as a trunk/limb fat ratio ≥ 1.5. Association between cumulative exposure to antiretroviral (measured in years of use) with trunk/limb fat ratio (coded as a continuous variable) was assessed using univariate and multivariate linear regression for each antiretroviral drug with at least 40 exposed patients. RESULTS: One hundred sixty-six patients were included. Seventy-five percent were male, median age was 56 years, 67% were Caucasian. Overall, prevalence of lipodystrophy was 47%, with a mean trunk/limb fat ratio of 1.87, SD = 1.03, min = 0.6 and max = 5.87. Each 10-year increase in age and HIV infection duration was associated with an average increase of 0.24 and 0.34 for the trunk/limb fat ratio respectively. (p = 0.003, p = 0.002, respectively) Patients classified as lipodystrophic were more likely to be diabetic (50 vs. 28%, p = 0.07) and to have dyslipidemia (47 vs. 19%, p = 0.01). According to viral load at DEXA test, each one log increase was associated with less probability (0.7) of lipodystrophy. (p = 0.01) Among ARV drugs tested, there was an association between years of use of d4T, ritonavir and raltegravir and higher trunk/limb fat ratio (indicating more lipodystrophy) (p < 0.05). CONCLUSION: Lipodystrophy is very common in HIV infected patients and is correlated with duration of some new antiretroviral drugs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".