Nutritional perspectives for the prevention and mitigation of COVID-19
Bibliographic record
Abstract
Worldwide, there is an array of clinical trials under way to evaluate treatment options against coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2. Concurrently, several nutritional therapies and alternative supportive treatments are also being used and tested to reduce the mortality associated with acute respiratory distress in patients with COVID-19. In the context of COVID-19, improved nutrition that includes micronutrient supplementation to augment the immune system has been recognized as a viable approach to both prevent and alleviate the severity of the infection. The potential role of micronutrients as immune-boosting agents is particularly relevant for low- and middle-income countries, which already have an existing high burden of undernutrition and micronutrient deficiencies. A systematic literature review was performed to identify nutritional interventions that might prevent or aid in the recovery from COVID-19. The PubMed, ScienceDirect, Cochrane, Scopus, Web of Science, and Google Scholar databases were searched electronically from February to April 2020. All abstracts and full-text articles were examined for their relevance to this review. The information gathered was collated under various categories. Deficiencies of micronutrients, especially vitamins A, B complex, C, and D, zinc, iron, and selenium, are common among vulnerable populations in general and among COVID-19 patients in particular and could plausibly increase the risk of mortality. Judicious use of need-based micronutrient supplementation, alongside existing micronutrient fortification programs, is warranted in the current global pandemic, especially in low- and middle-income economies.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".