Association of Hospitalization Rate, Mortality, and CD4 T Cell Count with Comorbidity of COVID-19 and HIV: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Epidemiological data demonstrate the greater severity of SARS-CoV-2 infection in HIV patients along with the more hospitalization, and mortality rates. Thus, this meta-analysis aimed to assess the possible differences in hospitalization, mortality, and the CD4 T cell counts between COVID-19/HIV co-infected patients and the control group. The relevant studies were obtained from online databases such as Science Direct, PubMed, Scopus, Web of Science, and Google Scholar using Mesh and Non-Mesh keywords and the meta-analysis was conducted according to the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols checklist. Then, the Newcastle-Ottawa scale (NOS) checklist was used to assess the quality of selected studies. According to the random effect models, the odds ratios of hospitalization, mortality, and CD4 T cell counts were estimated. The odds ratios of hospitalization and mortality rates in COVID-19 patients with HIV were 1.67 (confidence interval [CI]: 0.76 to 3.71) and 0.80 (CI: 0.57 to 1.11), respectively, compared to that of the COVID-19 group. In this meta-analysis, there was no statistically significant difference in the rates of hospitalization, mortality, and CD4 T cell counts between COVID-19 patients with HIV and the control group. The similarity between the studied groups could be attributed to factors such as the rarity of COVID-19/HIV co-infection patients and the presence of random error, administration of antiretroviral therapy in HIV patients, and early hospitalization time in COVID-19/HIV co-infected patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".