Coronavirus disease 2019, vitamin D and kidney function
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this review is to summarize the emerging studies analyzing the association between vitamin D and risk of COVID-19 infection and severity, as well as the early interventional studies investigating the protective effect of vitamin D supplementation against COVID-19. RECENT FINDINGS: Studies investigating the association between vitamin D levels and risk of COVID-19 infection and risk of severe disease and mortality among those infected have yielded mixed results. Thus far, the majority of studies investigating the association between vitamin D and COVID-19 have been observational and rely on vitamin D levels obtained at the time of admission, limiting causal inference. Currently, clinical trials assessing the effects of vitamin D supplementation in individuals with COVID-19 infection are extremely limited. Randomized, interventional trials may offer more clarity on the protective effects of vitamin D against COVID-19 infection and outcomes. SUMMARY: Decreased levels of vitamin D may amplify the inflammatory effects of COVID-19 infection, yet, data regarding the mortality benefits of vitamin D supplementation in COVID-19-infected individuals are still limited. Current observational data provides the impetus for future studies to including randomized controlled trials to determine whether vitamin D supplementation in COVID-19-infected individuals with kidney disease can improve mortality 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".