Cardiovascular Risk in HIV-Infected and Uninfected Postmenopausal Minority Women: Use of the Framingham Risk Score
Bibliographic record
Abstract
OBJECTIVE: To characterize and compare cardiovascular disease (CVD) risk in HIV-infected and uninfected postmenopausal minority women using the Framingham Risk Score (FRS) as an assessment measure. METHODS: A cross-sectional analysis was performed in 152 (109 HIV+, 43 HIV-) subjects from an existing study cohort of postmenopausal Hispanic and African American women. Data necessary to calculate FRS and menopause features were retrieved by retrospective chart review. Bivariate statistics was used to compare CVD risk factors. Multivariable linear regression was used to determine factors associated with FRS in HIV-infected women. RESULTS: The HIV-infected group was younger, less obese, and with lower rates of diabetes versus controls. In a subset of age-matched participants, median FRS did not differ between groups (14.6 [IQR = 9.1, 21.6] vs. 15.5 [IQR = 12.3, 22.1]; p = 0.73). Fourteen percent of HIV-infected women meeting criteria for the low-risk FRS category (<10%) had a history of CVD, a similar rate as controls. HIV-infected women at intermediate/high CVD risk had higher rates of surgical menopause. According to 2013 clinical guidelines, more than half of HIV-infected women not prescribed statin therapy (52%) were eligible for treatment; however, statin therapy was similarly under-prescribed in uninfected women. CONCLUSIONS: In this study, CVD risk as assessed by the FRS was not significantly different by HIV status. Performance of the FRS may be compromised in postmenopausal HIV-infected minority women. HIV-infected and uninfected women may be undertreated with statin therapy. Large longitudinal cohorts and inclusion of subclinical measures of CVD are necessary to better characterize risk.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| 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".