Relationship Between Blood Group and Risk of Infection and Death in COVID-19: a live Meta-Analysis
Bibliographic record
Abstract
Abstract Introduction The relationship between ABO blood group and the incidence of COVID-19 infection and death has been investigated in several studies. The reported results were controversial, so the objective of the present study is to assess the relationship between different blood groups and the onset and mortality of COVID-19 infection using meta-analysis method. Methods We searched the databases using appropriate MeSH terms. We screened articles on the basis of titles, abstracts, and full texts and the articles that met the inclusion criteria were selected. Quality assessment was done with the Newcastle-Ottawa Scale checklist. The estimated frequency of COVID-19 infection and death in terms of ABO blood group and the overall estimate of the odd ratio between blood group with COVID-19 infection and death was done with 95% confidence interval. Results The pooled frequency of blood groups A, B, O, and AB among COVID-19 infected individuals was estimated as 36.22%, 24.99%, 29.67%, and 9.29% respectively. The frequency of blood groups A, B, O, and AB among the dead cases due to COVID-19 infection was estimated as 40%, 23%, 29%, and 8% respectively. The odd ratio of COVID-19 infection for blood group A versus the other blood groups was estimated 1.16 (CI 95%: 1.02-1.33). The corresponding figures for blood groups O and AB versus other blood groups were estimated as 0.73 (CI 95%: 0.60-0.88) and 1.25(CI 95%: 0.84-1.86) respectively. Conclusion This meta-analysis showed that individuals with blood group A are at higher risk for COVID-19 infection while those with blood group O are at lower risk. Although the odds ratio of death for AB blood group was non-significant, it was considerable.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.046 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.005 | 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".