ABO blood group and COVID-19: an updated systematic literature review and meta-analysis.
Bibliographic record
Abstract
BACKGROUND: Following the first reports in the literature, the association between the ABO blood group and SARS-CoV-2 infection has been investigated by a number of studies, although with varying results. The main object of this systematic review was to assess the relationship between the ABO blood group and the occurrence and severity of COVID-19. MATERIALS AND METHODS: A systematic literature search using appropriate MeSH terms was performed through Medline and PubMed. The outcomes considered were the prevalence of the blood group O vs non-O types in SARS-CoV-2 infected and non-infected subjects, and the severity of SARS-CoV-2 infection according to ABO group. The methodological quality of the studies included in the analysis was assessed with the Newcastle-Ottawa Scale, and the overall quality of the available evidence using the GRADE system. Benchmarks used to evaluate the effect size were odd ratios (ORs) for case control studies and risk ratios (RRs) for cohort studies. RESULTS: Twenty-one studies were included in the analysis. Overall, individuals with group O had a lower infection rate compared to individuals of non-O group (OR: 0.81; 95% CI: 0.75, 0.86). However, the difference in the effect size was significantly lower in cohort studies compared to case control studies. No evidence was found indicating an effect of the O type on the disease severity in the infected patients. DISCUSSION: We have found low/very low evidence that group O individuals are less susceptible to SARS-CoV-2 infection compared to those in the non-O group. No evidence was found indicating an effect of the O type on disease severity in SARS-CoV-2 infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".