Aging with HIV vs. HIV Seroconversion at Older Age: A Diverse Population with Distinct Comorbidity Profiles
Bibliographic record
Abstract
OBJECTIVE: People aging with HIV might have different health conditions compared with people who seroconverted at older ages. The study objective was to assess the prevalence of, and risk factors for, individual co-morbidities and multimorbidity (MM) between HIV-positive patients with a longer duration of HIV infection, and patients who seroconverted at an older age. We compared estimates across both groups to a matched community-based cohort sampled from the general population. METHODS: We performed a case-control study including antiretroviral therapy (ART)-experienced patients who were HIV seropositive for ≥ 20.6 years ("HIV-Aging"), or who were seropositive for < 11.3 years ("HIV-Aged") having access in 2013 at the Modena HIV Metabolic Clinic. Patients were matched in a 1:3 ratio with controls from the CINECA ARNO database. MM was defined as the concurrent presence of >2 NICM. Logistic regression models were constructed to evaluate associated predictors of NICM and MM. RESULTS: We analysed 404 HIV-Aging and 404 HIV-Aged participants in comparison to 2424 controls. The mean age was 46.7 ± 6.2 years, 28.9% were women. Prevalence of HIV co-morbidities and MM were significantly higher in the HIV-positive groups compared to the general population (p<0.001) and a trend towards higher rates of MM was found in aging vs aged group. This difference turned to be significant in patients above the age of 45 years old (p<0.001). CONCLUSIONS: People aging with HIV display heterogeneous health conditions. Host factors and duration of HIV infection are associated with increased risk of MM compared to the general population.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".