Comorbidity is more common and occurs earlier in persons living with HIV than in HIV-uninfected matched controls, aged 50 years and older: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: At present, data are limited on the comorbidity profiles associated with aging people with HIV in the developing world, where most such people live. The aim of this study was to compare the disease burden between older HIV-positive subjects and HIV-negative matched controls in Brazil. METHODS: This was a cross-sectional analysis of the South Brazilian HIV Cohort. Individuals aged 50 years and older were enrolled at Hospital de Clínicas de Porto Alegre and matched with HIV-negative controls from the primary practice unit of the same hospital. Multimorbidity (the presence of two or more comorbid conditions) and the number of non-infectious comorbidities were compared. Poisson regression was used to identify factors associated with multimorbidity. RESULTS: A total of 208 HIV-positive subjects were matched to 208 HIV-negative controls. Overall, the median age was 57 years and 56% were male. The prevalence of multimorbidity was higher in HIV-positive subjects than in HIV-negative controls (63% vs. 43%, p<0.001), and the median number of comorbidities was 2, compared to 1 in controls (p<0.001). The duration of HIV infection (p=0.02) and time on treatment in years (p=0.015) were associated with greater multimorbidity in HIV-positive persons. CONCLUSIONS: In this large cohort from the developing world, multimorbidity was found to be more common in HIV-positive subjects than in HIV-negative controls. The duration of HIV and time on antiretrovirals were associated with multimorbidity.
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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 | 0.000 | 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".