The effects of high dose vitamin D supplementation as a nutritional intervention strategy on biochemical and inflammatory factors in adults with COVID-19: Study protocol for a randomized controlled trial
Bibliographic record
Abstract
Introduction: Low serum vitamin D has been shown to be a risk factor for Coronavirus 2019 (COVID-19). The aim of this study was to assess the effects of high dose vitamin D supplementation on hs-CRP, ESR and clinical outcomes, including duration of hospitalization, quality of life and New York Heart Association (NYHA) Functional Classification, in adults with COVID-19. Methods: This double-blind, randomized control trial will be conducted on patients with RT-PCR and/or chest CT scan diagnosis of COVID-19 admitted in Imam Reza Hospital, Mashhad, Iran. Participants will be randomized into control and intervention groups based on randomization sampling. The intervention group will receive soft gel containing 50,000 IU vitamin D on the first day followed by 10,000 IU/day through a supplement drop daily for 29 days. The control group will receive 1000 IU vitamin D daily through supplement drop and a placebo soft gel. All participants will undergo laboratory assessment including inflammatory markers, serum 25)OH)D, complete blood count (CBC), liver and renal profile, lipid profile and erythrocyte sedimentation rate (ESR) at baseline and at day 30. The mortality rate will be recorded in both groups. Results: Data will be presented using descriptive statistics. Comparison of changes in study parameters over the study period will be performed using analysis of covariance adjusting for possible confounders. Conclusions: The findings of this will provide evidence on the effects of high dose vitamin D supplementation on inflammatory markers in hospitalized COVID-19 patients.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".