Use of traditional Chinese medicine as an adjunctive treatment for COVID-19
Bibliographic record
Abstract
BACKGROUND: This review aims to evaluate the supportive effects of frequently used traditional Chinese medicine (TCM) for the treatment of coronavirus disease 2019 (COVID-19). METHODS: Five databases were searched through July 7, 2020. Randomized controlled trials investigating the efficacy of TCM for use in the treatment of COVID-19 were included. Newcastle-Ottawa Scale (NOS) and modified Jadad score were used for the evaluation of the methodological quality of the included studies. Weighted mean difference, odds ratio (OR), and 95% confidence interval (95% CI) were calculated for pooling out results. Data were extracted for conducting a meta-analysis using STATA version 12.0. RESULTS: Eight studies with a total of 750 patients were included in this meta-analysis. All included trial groups involved treatment with TCM and Western medicine, while the control groups were treated only with Western medicine. The intervention therapy significantly improved the overall effective rate (n = 346, OR = 2.5, 95% CIs = 1.46-4.29), fever symptom disappearance rate (n = 436; OR = 3.6; 95% CIs = 2.13-6.08), fatigue symptom disappearance rate (n = 436; OR = 3.04; 95% CIs = 1.76-5.26), cough symptom disappearance rate (n = 436; OR = 2.91; 95% CIs = 1.36-6.19), and sputum production reduction (n = 436; OR = 5.51; 95% CIs = 1.94-15.64). Based on the Newcastle-Ottawa Scale assessment, 6 studies received a score of 4, and 1 study achieved a score of 5. One study was assessed using the modified Jadad score, achieving a score of 6. CONCLUSIONS: The integration of TCM with Western medicine has significantly improved the treatment for COVID-19 patients compared to Western medicine treatment alone. Combined therapy using TCM and Western medicine revealed the potential adjunctive role of TCM in treating COVID-19. However, high-quality clinical studies are still required to further evaluate the efficacy and safety of TCM in the treatment of COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".