Correlation between ABO blood type, susceptibility to SARS-CoV-2 infection and COVID-19 disease severity: A systematic review
Bibliographic record
Abstract
OBJECTIVES: To verify the association between the ABO blood type and the risk of SARS-CoV-2 infection and COVID-19 disease severity. METHODS: This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), using the 2020 PRISMA Checklist and flow diagram, and articles selected for review were analyzed using the Newcastle-Ottawa Quality Rating Scale. The research question was: "Would the ABO blood group influence the risk of infection and clinical course of patients infected with SARS-CoV-2?", The following databases were used: Embase, PubMed, Virtual Health Library (VHL), Web of Science, ScienceDirect and Scopus. The protocol for this review was registered in the Prospective Register of Systematic Reviews (PROSPERO), number CRD42021245945. RESULTS: We found 798 articles across PubMed, Embase, Scopus, Web of Science, Science Direct and Virtual Health Library and 54 articles were included in the final analysis. Among 30 studies evaluating the risk of COVID-19 infection, 21 found significant correlations with ABO blood groups, 14 of them revealing an increased risk in blood group A and 15 studies showing a decreased risk in blood group O. Most studies found no significant correlation with disease severity or mortality. CONCLUSION: The qualitative assessment of available information suggests that blood group A may be a risk factor for COVID-19 infection and that blood group O may have a protective effect. We were unable to determine a clear association between the ABO blood group and mortality. These conclusions are based on highly heterogenous evidence.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".