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 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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.054 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".