Nutritional perspectives for the prevention and mitigation of COVID-19
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
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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