The Role of Nutraceuticals in the Prevention and/or Treatment of COVID-19: An Umbrella Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: To identify the results of published review literature regarding nutraceuticals, including probiotics, melatonin, poly-unsaturated fatty acids (PUFAs), quercetin, N-acetyl cysteine (NAC), and propolis as they relate to the prevention and/ or treatment of COVID-19 (CV) and/or long COVID (long CV) and to outline key areas to consider for clinical application and for further research. Methods: This paper is part of a six-part umbrella review which progresses from a living review. This review incorporates systematic reviews and narrative reviews as they relate to nutraceuticals. A live literature search occurred monthly in PubMed and Google Scholar from May 2022 to May 2023. Assessing the Methodological Quality of Systematic Reviews Version 2 (AMSTAR-2) scoring assessed systematic review quality, while the Scale for the Assessment of Narrative Review Articles (SANRA) guidelines evaluated narrative reviews. Only those studies that were relevant to the nutraceuticals outlined above and that addressed COVID-19 prevention and/or treatment of CV and/or long CV were extracted from each review. Results: Fifteen narrative reviews and 16 systematic reviews were included in this umbrella review. Studies indicate that nutraceuticals may be beneficial in improving the rate of recovery from various COVID-19 symptoms, rate of conversion parameters such as rate or duration of hospital stay and risk of intensive care unit (ICU) admission, and an improvement in various laboratory tests. Conclusion/Summary: The broad antioxidant, anti-inflammatory, antiviral, and immune modulatory characteristics make the nutraceuticals included in this review reasonable choices for further research. Of the nutraceuticals discussed above, probiotics, melatonin, NAC, and quercetin indicate the greatest potential for benefit in the prevention and treatment of COVID-19 and long CV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it