The Effect of HIV/AIDS Infection on the Clinical Outcomes of COVID-19: A Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Patients with HIV may be more likely to become severely ill from COVID-19. The present meta-analysis aims to determine the impact of HIV/AIDS infection on the clinical outcomes of COVID-19. METHODS: A comprehensive literature search was performed to identify relevant cohort studies to evaluate the association of HIV/AIDS infection with clinical outcomes of COVID-19. International databases, including PubMed (Medline), Web of Sciences, Scopus, and Embase, were searched from the emergence of the COVID-19 pandemic until January 2022. We utilized the risk ratio (RR) with its 95% confidence interval (95% CI) to quantify the effect of cohort studies. RESULTS: Twelve cohort studies were included in this meta-analysis, which examined a total number of 17,786,384 patients. Among them, 40,386 were identified to be HIV positive, and 17,745,998 were HIV negative. The pooled analyses showed HIV positive patients who were co-infected with SARS-CoV-2 were 58% more likely to develop a fever (RR=1.58; 95% CI: 1.42, 1.75), 24% more likely to have dyspnea (RR=1.24; 95% CI: 1.08, 1.41), 45% more likely to be admitted to ICU (RR=1.45; 95% CI: 1.26, 1.67), and 37% more likely to die from to COVID-19 (RR=1.37; 95% CI: 1.30, 1.45) than HIV negative patients. CONCLUSION: HIV/AIDS coinfection with COVID 19 increased the risk of fever, dyspnea, ICU admission, and mortality.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.077 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".