Clinical and cardiovascular characteristics from subjects with in COVID-19 and viral outbreaks
Bibliographic record
Abstract
Introduction: Lower respiratory tract infections remain the deadliest communicable disease worldwide. The relationship between cardiovascular diseases and viral infections is well known; for example, during the AH1N1 influenza pandemic, many patients developed acute cardiovascular disease. In the SARS-CoV2 pandemic, cardiovascular health has again become a challenge, with early reports showing cardiac damage in these patients. Objective: The study aims to describe the clinical characteristics of COVID-19 patients with an emphasis on cardiovascular compromises, compared with past outbreaks of influenza AH1N1, to identify prognostic factors of severity. Methods: A cross-sectional study of 72 subjects with a confirmed diagnosis of COVID-19 was conducted. Subjects were evaluated in two groups: 38 hospitalized patients and 34 patients in the Intensive Care Unit (ICU). Data from different outbreaks of influenza AH1N1 were then compared with this group. Results: The 34 subjects in the ICU had higher levels of high sensible troponin, D dimer, creatinine, and leukocytes compared with the 38 hospitalized subjects. The lymphocytes count was diminished in 85.29% of ICU subjects. When compared with AH1N1 patients, it was found that SARS-CoV2 patients were 10 years older on average. The proportion of overweight and obese SARS-CoV2 patients was double that in the influenza outbreaks. In addition, it was observed that a high number of SARS-CoV2 subjects presented with diabetes mellitus. Conclusion: There were various clinical and severity differences between each of these outbreaks. However, viral respiratory infection diseases such as SARS-CoV2 are a significant risk factor for acute ischemic, functional, and structural cardiovascular complications. The only way to combat this risk is a prevention approach, specifically through vaccines, but also through measures that force drastic changes in health policies to reduce perhaps the worst of pandemics, obesity, and its metabolic consequences.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.000 | 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".