Estimating the Relationship between Serum Electrolytes and COVID-19: A systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and purpose: There are controversies on the association between electrolytes and Coronavirus disease 2019 (COVID-19) and its severity. Studies on these issues may help in resolving ambiguities. The purpose of this study was to assess the association between electrolyte indices and being infected with COVID-19 and developing severe symptoms using a meta-analysis. Materials and methods: A thorough search was done in national and international electronic databases using Medical Subject Headings (MeSH) terms. Quality assessment was conducted by Newcastle-Ottawa scale (NOS) checklist. We estimated the standardized mean difference between electrolyte indices and the incident of COVID-19 infection and its severity. Results: After screening the papers, 12 met the inclusion criteria. According to the meta-analysis results, the standardized mean differences for serum level of sodium and potassium between the dead and survived COVID-19 patients was estimated to be 0.22 (95% CI: -0.03, 0.46) and 0.14 (95% CI: -0.22, 0.50), respectively. The standardized mean differences for serum levels of sodium, calcium, and potassium between patients with severe and non-severe COVID-19 were estimated to be -0.28 (95% CI: -0.72, 0.17), -1.07(95% CI: -1.58, -0.55), and -0.10 (95% CI: -0.47, 0.27), respectively. Conclusion: In this meta-analysis, the standardized mean difference for calcium was significantly lower in severe COVID-19 patients compared to that in patients with mild and moderate forms of the disease.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.054 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".