HIV Infection and Risk for Incident Pulmonary Diseases in the Combination Antiretroviral Therapy Era
Bibliographic record
Abstract
RATIONALE: In aging HIV-infected populations comorbid diseases are important determinants of morbidity and mortality. Pulmonary diseases have not been systematically assessed in the combination antiretroviral therapy (ART) era. OBJECTIVES: To determine the incidence of pulmonary diseases in HIV-infected persons compared with HIV-uninfected persons. METHODS: We analyzed data from the Veterans Aging Cohort Study Virtual Cohort, consisting of 33,420 HIV-infected veterans and 66,840 age, sex, race and ethnicity, and site-matched HIV-uninfected veterans. Using Poisson regression, incidence rates and adjusted incidence rate ratios were calculated to determine the association of HIV with pulmonary disease. The Virtual Cohort was merged with the 1999 Veterans Large Health Survey to adjust for self-reported smoking in a nested sample (14%). MEASUREMENTS AND MAIN RESULTS: Incident chronic obstructive pulmonary disease, lung cancer, pulmonary hypertension, and pulmonary fibrosis, as well as pulmonary infections, were significantly more likely among HIV-infected patients compared with uninfected patients in adjusted analyses, although rates of asthma did not differ by HIV status. Bacterial pneumonia and chronic obstructive pulmonary disease were the two most common incident pulmonary diseases, whereas opportunistic pneumonias were less common. Absolute rates of most pulmonary diseases increased with age, although the relative differences between those with and without HIV infection were greatest in younger persons. Chronic obstructive pulmonary disease and asthma, as well as pulmonary infections, were less likely in those with lower HIV RNA levels and use of ART at baseline. CONCLUSIONS: Pulmonary diseases among HIV-infected patients receiving care within the Veterans Affairs Healthcare System in the combination ART era reflect a substantial burden of non-AIDS-defining and chronic conditions, many of which are associated with aging.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".