Effects of Vitamin D Serum Level on Morbidity and Mortality in Patients with COVID-19: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: It has been shown that low Vitamin D serum concentration is associated with increased pneumonia and viral respiratory infections. Vitamin D is readily available, inexpensive, and easy to administer to subjects infected with COVID-19. If effective in reducing the severity of COVID-19, it could be an important and feasible therapeutic intervention. METHODS: We performed a systematic review and meta-analysis of the literature to determine the effects of Vitamin D serum concentration on mortality and morbidity in COVID-19 patients. The primary objectives were to determine if Vitamin D serum concentration decrease mortality, ICU admissions, ventilator support, and length of hospital stay in COVID-19 patients. RESULTS: A total of 3572 publications were identified. Ultimately, 20 studies are included. A total of 12,806 patients aged between 42 to 81 years old were analyzed. The pooled estimated RR for mortality, ICU admission, ventilator support and length of hospital stay were 1.49 (95% CI: 1.34, 1.65), 0.87 (95% CI: 0.67, 1.14), 1.29 (95% CI: 0.79, 1.84), and 0.84 (95% CI -0.45, 2.13). CONCLUSION: There is no statistical difference in mortality, ICU admission rate, ventilator support requirement, and length of hospital stay in COVID-19 patients with low and high Vitamin D serum concentration.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".