Contribution of traditional Chinese medicine combined with conventional western medicine treatment for the novel coronavirus disease (<scp>COVID</scp>‐19), current evidence with systematic review and meta‐analysis
Bibliographic record
Abstract
This study provides current evidence for efficacy and safety of treating COVID-19 with combined traditional Chinese medicine (TCM) and conventional western medicine (CWM). Six databases were searched from January 1 to December 31, 2020. Randomized controlled trials (RCTs), case-control studies (CCTs), and cohort studies on TCM or TCM combined with CWM treatment for COVID-19 were included. The quality of included RCTs was assessed by Cochrane risk of bias tool, and the Newcastle-Ottawa Scale (NOS) was used to assess the quality of cohort studies and CCTs. Review Manager 5.4 software was used to perform meta-analysis. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. A total of 35 studies (3,808 patients) composing 19 RCTs and 16 observational studies were included. The results of meta-analysis revealed that comparing with CWM alone, integrated TCM and CWM had significant improvement in total effective rate, improvement rate of chest CT, the rate of disease progression, as well as improvement of fever, fatigue and cough. The overall quality of evidence was very low to moderate. In conclusion, TCM combined with CWM was a potential treatment option for increasing clinical effective rate, improving the clinical symptoms, and preventing disease progression in COVID-19 patients. High-quality clinical trials are required in the further.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".