Metabolic Syndrome and Its Components in Patients with COVID-19: Severe Acute Respiratory Syndrome (SARS) and Mortality. A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Recent meta-analysis studies have reported that metabolic comorbidities such as diabetes, obesity, dyslipidaemia and hypertension are associated with higher risk of severe acute respiratory syndrome (SARS) and mortality in patients with COVID-19. This meta-analysis aims to investigate the relationship between metabolic syndrome (MetS) and its components with SARS and mortality in COVID-19 patients. Methods: A systematic search was conducted in the several databases up until 1 September 2021. Primary observational longitudinal studies published in peer review journals were selected. Two independent reviewers performed title and abstract screening, extracted data and assessed the risk of bias using the Newcastle–Ottawa Scale. Results: The random effects meta-analysis showed that MetS was significantly associated with SARS with a pooled OR (95% CI) of 3.21 (2.88–3.58) and mortality with a pooled OR (95% CI) of 2.32 (1.16–4.63). According to SARS, the pooled OR for MetS was 2.19 (1.71–2.67), p < 0.001; significantly higher than the hypertension component. With regard to mortality, although the pooled OR for MetS was greater than for its individual components, no significant differences were observed. Conclusions: this meta-analysis of cohort studies, showed that MetS is better associated to SARS and mortality in COVID-19 patients than its individual components.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.044 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".