Cross-sectional comparison of age- and gender-related comorbidities in people living with HIV in Canada
Bibliographic record
Abstract
Because antiretroviral therapy (ART) is allowing people living with human immunodeficiency virus (PLWH) to survive longer, they are developing more age-related comorbidities. We evaluated the effects of age and gender on the burden of age-related comorbidities among PLWH. In this retrospective real-world study, de-identified data were extracted from the medical charts of 2000 HIV-positive adults on ART across 10 sites in Canada. The prevalence of age-related comorbidities was determined in 6 age subgroups (<30, 30-39, 40-49, 50-59, 60-69, and ≥70 years). The effects of gender on these comorbidities were also examined. Risks of cardiovascular disease and chronic kidney disease (CKD) were calculated using the Framingham and D:A:D equations. Most persons were White (68%), male (87%), and virologically suppressed (94%). The mean age was 50.3 years (57% aged ≥50 years), and mean CD4+ T-cell count was 616 cells/mm3. The most common comorbidities were neuropsychiatric symptoms (61%), overweight/obesity (43%), liver disease (37%), and dyslipidemia (37%). The mean number of comorbidities increased across age subgroups (P < .001). Across all age subgroups, the prevalence of hypertension (P = .04), dyslipidemia (P = .04), CKD (P = .03), bone fragility (P = .03), and depression (P = .02) differed between males and females. Both age (P < .001) and gender (P < .001) impacted cardiovascular disease and CKD risk. Age and gender influenced the burden, types, and risks of age-related comorbidities in PLWH in this Canadian cohort. These comorbidities should be diagnosed and treated in routine clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".