Cardiovascular Disorders as a Risk Factor for Severe Covid-19: A Systematic Literature Review
Bibliographic record
Abstract
ObjectivesThis systematic review examined the relationship between cardiovascular disorders and severe COVID-19 outcomes.The study aimed to quantify the extent to which individuals with cardiovascular disorders are at risk of developing severe COVID-19 compared to those without these conditions. MethodologyA comprehensive search was conducted across PubMed, ScienceDirect, Google Scholar, and the Cochrane Library databases.Keywords used included "COVID-19," "SARS-CoV-2," "coronavirus," "cardiovascular disorders," "hypertension," "coronary artery disease," "heart failure," "atrial fibrillation," "risk factor," and "severe."The search was limited to articles published between 2020 and 2023 and written in English.The quality of the studies was assessed using the Newcastle-Ottawa quality assessment tool. ResultsAn initial search identified 3,059 studies (Google Scholar = 1,073; ScienceDirect = 752; PubMed = 1,234).After applying the eligibility criteria, 37 articles were selected for inclusion.Individuals with cardiovascular disorders were found to be significantly more likely to experience severe COVID-19 outcomes, with an odds ratio (OR) of 1.88 (95% CI: 1.32-2.70)for hospitalization and an OR of 3.576 (95% CI: 1.694-7.548)mortality.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".