Traditional Chinese medicine as an adjunctive therapy for mild and common COVID-19
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) in many countries is still very serious. At present, there is no specific and effective drug for this disease. Traditional Chinese medicine (TCM) has played a great role in fighting against COVID-19. However, their effectiveness and safety are still obscure and deserve further investigation. The aim of the study was to evaluate the efficacy and safety of TCM assisted in conventional treatment in the treatment of mild and common COVID-19. METHODS: PubMed, EMbase, MEDLINE, China National Knowledge Infrastructure Database, WANFANG DATA, and VIP Chinese Science and Technology Periodical Database were searched for randomized controlled trials (RCTs) and non-randomized controlled trials of TCM assisted in conventional treatment. The RCT research quality was evaluated by Cochrane 5.1.0 bias risk scale and the non-randomized controlled trial research quality was evaluated by Newcastle Ottawa scale, and the statistical analysis was conducted by Revman 5.3 and R software. The bias and sensitivity of the statistical results were analyzed by STATA 14.0. Registration number: CRD42020210619. RESULTS: Fifteen studies were included with 7 RCT studies and 8 retrospective cohort studies, involving a total of 1623 patients. Compared with the control group, TCM can improve the main index clinical effective rate (odds ratio [OR] = 2.64, 95% Confidence interval (CI) [1.94,3.59], P < .00001). The results of Begg test (Pr > z = 0.266) and sensitivity analysis showed that the results were relatively stable. Toujie Quwen (OR = 4.9, 95%CI [1.9,14.0]), Shufeng Jiedu (OR = 2.9, 95%CI [1.5,5.7]), and Lianhua Qingwen (OR = 2.4, 95%CI [1.6,3.6]) were with the best. It can also improve the main clinical symptoms (fever, cough, fatigue, and the regression time of the 3 symptoms), severe conversion rate, and computed tomography improvement rate. Its safety was not significantly compared with conventional treatment. However, in terms of safety of a single TCM, Shufeng Jiedu (OR = -0.86, 95%CI [-1.89,0.09]) and Lianhua Qingwen (OR = -0.49, 95%CI[-0.94,-0.05]) were lower than those of conventional treatment. CONCLUSION: TCM as an adjuvant therapy combined with conventional treatment has good curative effect on mild and common type of COVID-19 patients. Its advantages lie in clinical efficacy and improvement of symptom group, and can prevent patients from transforming to severe disease. In terms of clinical efficacy and safety, Shufeng Jiedu and Lianhua Qingwen have obvious advantages, which are worthy of clinical promotion.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".