The effect of Chinese herbal medicine on digestive system and liver functions should not be neglected in COVID‐19: An updated systematic review and meta‐analysis
Bibliographic record
Abstract
Gastrointestinal symptoms and liver injury are common in patients with coronavirus disease 2019 (COVID-19). However, profiles of different pharmaceutical interventions used are relatively underexplored. Chinese herbal medicine (CHM) has been increasingly used for patients with COVID-19, but the efficacy of CHM used in COVID-19 on gastrointestinal symptoms and liver functions has not been well studied with definitive results based on the updated studies. The present study aimed at testing the efficacy of CHM on digestive symptoms and liver function (primary outcomes), the aggravation of COVID-19, and the time to viral assay conversion (secondary outcomes), among patients with COVID-19, compared with standard pharmacotherapy. The literature search was undertaken in 11 electronic databases from December 1, 2019 up to November 8, 2020. Appraisal of the evidence was conducted with Cochrane risk of bias tool or Newcastle Ottawa Scale. A random-effects model or subgroup analysis was conducted when significant heterogeneity was identified in the meta-analysis. The certainty of the evidence was assessed with the grading of recommendations assessment, development, and evaluation approach. Forty-eight included trials involving 4,704 participants were included. Meta-analyses favored CHM plus standard pharmacotherapy for COVID-19 on reducing the aggravation of COVID-19 and the time to viral assay conversion compared with standard pharmacotherapy. However, the present CHM as a complementary therapy for treating COVID-19 may not be beneficial for improving most gastrointestinal symptoms and liver function based on the current evidence. More well-conducted trials are warranted to confirm the potential efficacy of CHM furtherly.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".