Ethnicity and Gender Differences in Lipodystrophy of HIV-Positive Individuals Taking Antiretroviral Therapy in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: This study assessed ethnicity and gender differences in prevalence, type, and severity of antiretroviral-associated lipodystrophy in HIV-positive individuals in Ontario. METHODS: This was a cross-sectional analysis of the Ontario Cohort Study (OCS), a prospective study of HIV-positive patients in Ontario. Lipodystrophy was defined as at least 1 major or 2 minor self-reported changes of peripheral lipoatrophy and/or central lipohypertrophy. Prevalence, type, and severity were compared by ethnicity (Black, White, or Other) and gender. Univariate and multivariate logistic regression analyses identified predictors of lipodystrophy. RESULTS: Data were available for 778 participants (659 men, 119 women). There were 517 Whites, 121 Blacks, and 140 patients of Other ethnicities. In univariate analyses, Whites reported more peripheral lipoatrophy (P = .004) and abdominal lipohypertrophy (P = .04); these ethnic differences were observed in males (P = .05 and P = .03, respectively) but not females. Males reported more peripheral lipoatrophy (P = .01), whereas females had more central lipohypertrophy (P < .0001) and mixed fat redistribution (P < .0001). Multivariable regression analyses revealed Black women to be most vulnerable to lipodystrophy (P = .02), particularly lipohypertrophy (P < .0001). CONCLUSIONS: Ethnicity and gender are important factors influencing lipodystrophy. Combining lipoatrophy and lipohypertrophy into a single entity is not appropriate. Black women were most vulnerable to lipohypertrophy, which has important implications for antiretroviral therapy roll-out in Africa.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".