Efficacy and Safety of Traditional Chinese Medicine (Lianhua Qingwen) for Coronavirus Disease 2019: a Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background: The coronavirus disease 2019(COVID-19) had become an epidemic and spread across the world, lead to severe respiratory failure and death. Traditional Chinese Medicine(TCM), Such as Lianhua Qingwen has been widely used in the prevention and treatment of COVID-19, This systematic review and meta-analysis will assess the effects of traditional Chinese herbal medicine(Lianhua Qingwen) in COVID-19 pneumonia from the randomized controlled trials(RCTs) and case control studies(CCSs).Method: we search the literatures in databases inculding PubMed, Embase, Web of science, Cochrane Library, Wanfang, Chinese Science and Technology Periodical Database (VIP), Chinese Biomedical Literature Database (CBM) and China National Knowledge Infrastructure(CNKI), setting the date from December 1, 2019, to June 1, 2021, Cochrane Risk of Bias tool and the Newcastle-Ottawa Scale were used to assess the quality of randomized controlled trials. All analyses were conducted by Stata 14.0.Results: nine studies with 1163 patients(616males) were included, six were RCTs, three are CCSs. Compared with patients treated by western medicine alone, patients treated by Lianhua Qingwen combined with western medicine have a higher overall effective rate[RR=1.20, 95%CI1.20(1.11, 1.31), P=0.000], cardinal symptom disappearance rate[disappearance rate of fever: OR:3.64, 95%CI(1.57, 8.47), P=0.001;disappearance rate of cough: OR:1.97, 95%CI(1.45, 2.68), P=0.001; disappearance rate of fatigue: OR:2.55, 95%CI(1.09, 5.99), P=0.032] and CT recovery rate[RR:1.25, 95%CI(1.13,1.38), P=0.000], reduce the rate of the progress into severe diseases of COVID-19 patients[RR:0.43, 95%CI(0.30, 0.62), P=0.000], with more shorter duration of fever[WMD=-1.07，95%CI(-1.77, -0.37), P=0.003], The including studies described that Lianhua Qingwen did not increase the adverse drug reactions.Conclusion: Lianhua Qingwen may have advantages in improving the clinical effective rate and cardinal symptom disappearance rate. Besides, it also had an excellent effect on the improvement of the chest CT and the proportion reducing of progress into severe clinical disease, which could be used as an effective therapy for COVID-19.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".