Efficacy and safety of integrated traditional Chinese and Western medicine against <scp>COVID</scp>‐19: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Although plenty of clinical trials have confirmed the efficacy and safety of integrated traditional Chinese and Western medicine (ITCWM) against COVID‐19, the role of ITCWM remains controversial. So we conducted a systematic review and meta‐analysis of published studies in eight major databases that report the outcomes of interest in COVID‐19 patients receiving ITCWM. RevMan5.4 software was used for meta‐analysis, while the quality of RCTs was assessed by the Cochrane risk of bias tool and the retrospective studies were assessed by Newcastle–Ottawa Scale. Eventually, a total of 53 studies with 5425 COVID‐19 patients was identified. The meta‐analysis results showed that ITCWM was significantly better than western medicine treatment (WMT) alone in the percentage of cases changing to severe/critical [RR = 0.40, 95%CI (0.33, 0.49), p < .00001, I2 = 10%], overall clinical effectiveness [RR = 1.26, 95% CI (1.18, 1.35), p < .00001, I2 = 50%], time to defervescencer [MD = −1.45, 95% CI (−1.82, −1.07), p < .00001, I2 = 83%], disappearing time of cough [MD = −2.11, 95% CI (−2.98, −1.25), p < .00001, I2 = 93%], time of RT‐PCR negativity [MD = −3.35, 95% CI (−4.74, −1.95), p < .00001, I2 = 92%], length of hospital stay [MD = −4.05, 95% CI (−5.24, −2.85), p < .00001, I2 = 91%], improvement in CT scan [RR = 1.22, 95% CI (1.17, 1.28), p < .00001, I2 = 46%], TCM syndrome score [MD = −3.95, 95% CI (−5.07, −2.82), p < .00001, I2 = 92%], disappearance rate of fever [RR = 1.23, 95% CI (1.10, 1.38), p < .00001, I2 = 85%], disappearance rate of cough [RR = 1.43, 95% CI (1.25, 1.63), p < .00001, I2 = 60%], level of CRP [MD = −9.23, 95% CI (−10.94, −7.52), p < .00001, I2 = 97%], and WBC [MD = −9.23, 95% CI (−10.94, −7.52), p < .00001, I2 = 97%]. There is no significant difference between ITCWM and WMT in the adverse reaction rate [RR = 0.85, 95% CI(0.71, 1.03), p = .10, I2 = 25%]. Our results showed evidence of clinical efficacy and safety benefit in COVID‐19 patients treated with ITCWM. In spite of some limitations, the rapidly developing global pandemic warrants further high‐quality and multicenter clinical studies to confirm the contribution of ITCWM.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.031 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".