HIV infection and risk of COVID-19 mortality
Bibliographic record
Abstract
BACKGROUND: This meta-analysis aimed to estimate the association of human immunodeficiency virus (HIV) infection and risk of coronavirus disease 2019 (COVID-19) mortality. METHODS: We systematically retrieved articles published on HIV infection and risk of COVID-19 mortality through PubMed, EMBase, China National Knowledge Infrastructure, WanFang, and Chongqing VIP databases using a predefined search strategy from December 1, 2019 to January 31, 2021. Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included studies. Cochran Q test and I2 statistics were quantified to measure heterogeneity. Odds ratio (OR) and 95% confidence intervals (CI) were computed and displayed in the form of forest plots. Subgroup analysis was performed to explore the source of heterogeneity. Funnel plot, Begg test, and Egger test were used to assess potential publication bias. Stata software version 11.0 was used to analyze all the statistical data. RESULTS: We included 10 studies with 18,122,370 COVID-19 patients, of whom 41,113 were with HIV infection and 18,081,257 were without HIV infection. The pooled overall results suggested that people living with HIV infection had a higher risk of mortality from COVID-19 than those without HIV infection (OR = 1.252, 95% CI 1.027-1.524). Subgroup analysis showed that people living with HIV infection had a higher risk of COVID-19 mortality than those without HIV infection in the United States (OR = 1.520, 95% CI 1.252-1.845) and in South Africa (OR = 1.122, 95% CI 1.032-1.220); however, no significant association was found in the United Kingdom (OR = 0.878, 95% CI 0.657-1.174). CONCLUSION: Patients with HIV infection should be the emphasis population to prevent the risk of mortality during the clinical treatment of COVID-19 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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.032 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".