SARS-CoV-2: Review of Conditions Associated With Severe Disease and Mortality
Bibliographic record
Abstract
The 2019 Coronavirus Virus Disease (COVID-19) represents a global public health challenge in the twenty-first century. As of June 2020, the virus had spread across 216 countries across the globe. This paper aims to analyze and identify those existing comorbidities among COVID-19 patients that represent potential risk factors for COVID-19 complications, severe illness, and death. Multiple database resources were searched. The resources include the University of Saskatchewan library USearch, Google Scholar, PubMed, Medline, and the Google search engine. Thirty-seven articles, which included 15 different types of chronic diseases, were selected. Among the reviewed diseases and conditions, cancer, diabetes, lymphopenia, hypertension, kidney disease, smoking, chronic obstructive pulmonary disease (COPD), and organ transplant were found to represent potential risk factors for COVID-19 complications, severe illness, and death. Other conditions that require further research as to whether they predispose subjects to severe illness and death include coronary artery disease, cerebrovascular disease, valvular heart disease, gastrointestinal diseases, HIV/AIDS, asthma, and liver disease. In conclusion, this article explains the association between diseases mentioned above and the severity of COVID-19 and clearly shows the population at risk. This paper will help government bodies and decision-makers prioritize resources for these populations to reduce mortality rates and overall quality of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".