Associations between Plant-Based Food Intake and COVID-19 Outcomes: A Study Protocol
Bibliographic record
Abstract
Introduction: Coronavirus disease 2019 (COVID-19) left a lasting impact on the world where it not only had serious implications to the healthcare system, but also on individuals at a higher risk for other illnesses. In an effort to protect themselves from experiencing severe symptoms of COVID-19, other alternatives to alleviate the risk of the viral infection are being investigated. As documented in previous research, adopting a plant-based diet improves immunity against viruses. Hence, this study aims to investigate the associations between plant-based diets and the effects of COVID-19. Methods: This is a cross-sectional study of 500 adults between the ages of 18 and 65 who have tested positive for COVID-19 in 2022. Participants are to fill out food-frequency and COVID-19 questionnaires. To detect significance, an ANOVA is to be used to compare the dietary patterns in each COVID-19 severity group (ASYMP, MILD, MOD, SEV, CRI). Results: Expectedly, participants with the highest alongside most frequent intake of plant-based foods and seafood exhibit mild COVID-19 symptoms based on related studies. Whereas participants with the lowest intake of plant-based foods are expected to display symptoms corresponding to the critical and severe COVID-19 groups. Discussion: Foods derived from plants are rich in vitamins and minerals that function as anti-inflammatories and boost immunity against COVID-19. Plant-based diets are comprised of low-fat, low-sodium foods, resulting in lower BMIs and risk of comorbidities like heart disease which further reduces the risk of severe COVID-19. Conclusion: Considering this, a plant-based diet can be proposed as a lifestyle change that may aid in managing COVID-19. Through this protocol, the direct association between diet and COVID-19 will highlight the importance of nutrition for immunity as other infectious diseases continue to emerge.
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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
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".