Effect of serum magnesium levels on outcomes of patients hospitalized with COVID-19
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) causes acute respiratory illness and multi-organ failure. The critical roles of magnesium in human health suggest that it could have an active role in the prevention and treatment of COVID-19. We measured magnesium levels in hospitalized COVID-19 patients concerning disease progression and mortality. MATERIALS AND METHODS: This study was conducted in 2321 hospitalized COVID-19 patients. Clinical characteristics from each patient were recorded, and blood samples were collected from all patients upon their first admission to the hospital to determine serum magnesium levels. Patients were divided into two groups based on discharge or death. The effects of magnesium on death, severity, and hospitalization duration were estimated by crude and adjusted odds ratio using Stata Crop (version 12) software. RESULTS: Mean magnesium levels in patients who died were higher than in discharged patients (2.10 vs 1.96 mg/dl, p 0.05). CONCLUSIONS: We found no relation between hypomagnesaemia on COVID-19 progression, although hypermagnesaemia could affect COVID-19 mortality (Tab. 4, Ref. 34).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".