Relationships Between Serum Vitamin D, Inflammatory Markers, and Outcomes in Non‐Critically Ill Patients With COVID‐19: A Cross‐Sectional Study
Bibliographic record
Abstract
ABSTRACT Backround Treatment options for COVID‐19 remain limited and are primarily focused on specific patient populations; accordingly, preventive measures continue to be a crucial aspect of effective management. There is evidence that vitamin D effectively prevents viral upper respiratory tract infections during epidemics. The aim of this study was to assess the association between serum vitamin D and inflammatory markers, mortality, and clinical symptoms in patients with COVID‐19. Methods This cross‐sectional study involved non‐critically ill patients with COVID‐19 in a provincial reference hospital in Mashhad, Iran. Demographic and clinical data were extracted from patient medical records. Serum vitamin D was measured for each patient within 12 h of admission. Data were analyzed using linear and logistic regression models. Results In total, 452 patients (mean age 63.87 ± 17.97 years) were included in this study during 2 months of data collection. The most common serum vitamin D status was sufficient (30.0%), followed by deficient (29.4%), insufficient (23.2%), and severely deficient (17.3%). Partial symptom improvement was observed in 326 (72.1%) patients after 22 days of hospitalization, disregarding the vitamin D status. The mortality rate was 22.6%. COVID‐19 mortality was significantly related to serum urea (p = 0.002, OR = 1.020, 95% CI: 1.008–1.033), pulse rate (p = 0.015, OR = 1.047, 95% CI: 1.009–1.086), and age (p = 0.002, OR = 1.076, 95% CI: 1.027–1.127). Conclusions Among patients with COVID‐19, serum vitamin D levels were linked to mortality and some clinical parameters, including urea and pulse rate. Further longitudinal studies should evaluate the relationship between serum vitamin D levels and COVID‐19 outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".