Efficacy and safety of herbal medicine (Lianhuaqingwen) for treating COVID-19: A systematic review and meta-analysis
Bibliographic record
Abstract
Lianhuaqingwen (LH) has been proven effective for influenza. However, the promotion of LH for the treatment of patients with COVID-19 remains controversial. Therefore, our study aimed to assess the efficacy and safety of Lianhuaqingwen (LH) in treating patients with COVID-19 by a systematic review and meta-analysis. We conducted the literature search using six electronic databases from December 1, 2019, to June 2, 2020. Cochrane Risk of Bias tool was used to assess the quality of randomized controlled trials. Newcastle-Ottawa Scale was used to assess the quality of case control studies. Agency for Healthcare Research and Quality checklist was used to assess the quality of case series. All analyses were conducted by RevMan 5.3. For outcomes that could not be meta-analyzed were performed a descriptive analysis. Eight studies with 924 patients were included. Three studies were RCTs, three were case control studies, and two were case series. The quality of the included studies was poor. Compared with patients treated by conventional treatment, patients treated by LH combined with conventional treatment have a higher overall effective rate (RR = 1.16, 95%CIs: 1.04∼1.30, P = 0.01) and CT recovery rate (RR=1.21, 95%CIs: 1.02∼1.43, P = 0.03). Patients of LH groups have a lower incidence of diarrhea (5.6% vs.13.4%), and have statistically significant (P = 0.026). But the rate of abnormal liver function in the combined medication group is higher than that in the single LH group. LH combined with conventional treatment seems to be more effective for patients with mild or ordinary 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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".