Phytotherapy and Dietotherapy of COVID-19—An Online Survey Results from Central Part of Balkan Peninsula
Bibliographic record
Abstract
Since the appearance of the novel coronavirus disease of 2019-COVID-19, various therapeutic approaches were attempted, with complementary and alternative medicine (CAM) taking an important place. The aim of this study was to investigate the use of CAM with the purpose of prevention or treatment of COVID-19 during the pandemics. A prospective, cross-sectional study, in the form of an on-line survey was conducted. A total of 1704 responses were collected. Among the respondents, 2.76% declared currently and 22.12% previously having COVID-19. Approximately one quarter of interviewees (23.24%) declared themselves as phytotherapy users. The most frequently used medicinal plants were garlic, ginger and chamomile. The majority of respondents stated that they expected positive effects on immune and respiratory system. Medicinal plants were frequently used, on a daily basis. The main sources of information for applied self-medication were populistic thematic literature, followed by the Internet. Approximately one-third of phytotherapy users (35.25%) consulted with a medical professional before the application of phytotherapy. Regarding dietotherapy, 41.14% of respondents reported using non-herbal dietary supplements, while only 7.16% reported specific diet. The presented results suggest that CAM is recognized and readily used as a potential alternative and complementary regimen in the fight against COVID-19.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".