Oral Chinese Herbal Medicine on Immune Responses During Coronavirus Disease 2019: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Cellular immune responses including lymphocyte functions and immune effector cells are critical for the control of coronavirus infection. Chinese herbal medicine (CHM) potentially has a therapeutic effect for treatment of coronavirus disease 2019 (COVID-19). Nevertheless, there are limited clinical practice suggestions on immunogenicity of the CHM against SARS-CoV-2. To assess the effect of oral CHM on immunogenicity and whether oral CHM improves the clinical parameters through the immunity profile during COVID-19, we performed the present study. METHODS: For this systematic review and meta-analysis, 11 databases were searched for relevant studies assessing oral CHM for COVID-19 on November 20, 2020 (updated March 9, 2021). Primary outcomes mainly included immunity profiles. Secondary outcomes included all-cause mortality; the remission time of fever, cough, chest tightness, and fatigue. The random effect was used to estimate the heterogeneity of the studies. Summary relative risks, weight mean difference and standardized mean difference were measured with 95% confidence intervals. Modified Jadad scale and Newcastle-Ottawa Scale were used to assess the risk of bias of randomized controlled trials (RCTs) and observational studies, respectively. The certainty of evidence was evaluated using the GRADE approach. RESULTS: with moderate quality of evidence; and reduced TNF-α with low certainty of evidence. Besides, oral CHM, as an adjuvant medicine reduced the time to clinical symptoms remission with a lower risk of all-cause mortality, compared with routine treatment alone. CONCLUSION: CHM may be recommended as an adjuvant immunotherapy for disease modification and symptom relief in COVID-19 treatment. However, large RCTs objectively assessing the efficacy of CHM on immune responses in COVID-19 are needed to confirm our findings.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.028 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".