[Meta analysis on the treatment of coronavirus disease 2019 by traditional Chinese and Western medicine].
Bibliographic record
Abstract
OBJECTIVE: To evaluate the clinical efficacy and safety of combination of traditional Chinese and Western medicine in the treatment of coronavirus disease 2019 (COVID-19) by Meta analysis. METHODS: The clinical randomized controlled trials (RCT) and cohort studies on the treatment of COVID-19 with combination of Chinese traditional and Western medicine published on CNKI, Wanfang database, VIP database and PubMed were searched by computer from January 2020 to June 2020. Patients in the simple Western medicine treatment group were treated with routine treatment of Western medicine, and the patients in integrated traditional Chinese and Western medicine treatment group were treated with traditional Chinese medicine on the basis of routine treatment of Western medicine. The main outcome was the total effective rate of treatment. The secondary outcome were the antipyretic rate, chest CT recovery rate, lymphocyte count (LYM), C-reactive protein (CRP) level and safety. The Cochrane manual and the Newcastle Ottawa Scale (NOS) were used to evaluate the quality of the literature; the RevMan5.3 software was used to analyze the articles that meets the quality standards, and a funnel chart was drawn to evaluate the total effective publication bias. RESULTS: Thirteen articles were analyzed, including 1 039 COVID-19 patients, 559 in integrated traditional Chinese and Western medicine treatment group and 480 in simple Western medicine treatment group. The results of Meta- analysis showed that compared with the simple Western medicine treatment group, the combination of routine treatment of Western medicine and traditional Chinese medicine Qingfei Paidu decoction, Lianhua Qingwen granule, Shufeng jiedu capsule, Xuebijing injection or Reyanning mixture could significantly improve the total effective rate, antipyretic rate and chest CT recovery rate [total effective rate: odds ratio (OR) = 2.95, 95% confidence interval (95%CI) was 2.10-4.14, P < 0.000 01; antipyretic rate: OR =3.01, 95%CI was 1.64-5.53, P = 0.000 4; chest CT recovery rate: OR = 2.53, 95%CI was 1.83-3.51, P = 0.000 1], increase LYM levels [mean difference (MD) = 0.26, 95%CI was 0.02-0.50, P = 0.03], and reduce of CRP content (MD = -17.68, 95%CI was -33.14 to -2.22, P = 0.02). Based on the funnel chart analysis of 12 articles with total efficiency, the result showed that the funnel chart distribution was not completely symmetrical, indicating that there might be publication bias. CONCLUSIONS: On the basis of routine treatment with Western medicine, combined with traditional Chinese medicine can significantly improve the total effective rate of COVID-19 and improve the laboratory results and clinical symptoms of patients. Compared with the routine treatment of Western medicine alone, the combination of traditional Chinese and Western medicine has better clinical efficacy and safety.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".