Treating COVID-19 by traditional Chinese medicine: a charming strategy?
Bibliographic record
Abstract
On April 14, 2020, the State Council of China announced that “three proprietary Chinese medicines and three decoctions” of effective traditional Chinese medicine (TCM) in the treatment of the novel coronavirus pneumonia have passed clinical practice screenings [1]. Some scholars believe that early TCM intervention of mild and moderate cases and recovery period may reduce the ratio of mild cases progressing into severe and critical cases. Some data have also suggested that the combination of TCM and Western medicine may reduce the mortality rate in severe and critical cases [2, 3]. \n \n The “three proprietary Chinese medicines” are Chinese patent drug Jinhua Qinggan granules (approval number of State Food and Drug Administration of China (SFDA) Z20160001), Lianhua Qingwen granules (SFDA approval number Z20100040), and Xuebijing injection (SFDA approval number Z20040033) [4]. In this regard, the National Medical Products Administration of China recently approved the inclusion of the treatment of the novel coronavirus pneumonia as a new indication of the “three proprietary Chinese medicines”, which have become the world’s first batch of drugs suitable for COVID-19 (Table 1) [5]. \n \n The “three decoctions” are empirical formula of Chinese medicine Qingfei Paidu decoction, Huashi Baidu decoction, and Xuanfei Baidu decoction (Table 2). Among them, Qingfei Paidu granules and Huashi Baidu granule have been recently approved for clinical trials [5]. \n \n Jinhua Qinggan granule is a proprietary Chinese medicine developed during the 2009 H1N1 influenza pandemic. Both Lianhua Qingwen capsule and Xuebijing injection were identified as proprietary Chinese medicines developed and listed during the SARS in 2003 [3–5]. At present, 22 TCM treatment programs have been registered for clinical trials. \n \n However, because Lianhua Qingwen capsule contains Houttuynia cordata, Qingfei Paidu decoction contains Belamcandae Rhizoma, and both of them contain aristolochic acid, which can cause kidney injury and liver cancer [6–7]. Xuanfei Baidu decoction and Qingfei Paidu decoction contain Ephedrae Herba, which has cardiovascular toxicity and stimulative effect on central nervous system [8]. Qingfei Paidu decoction contains Alismatis Rhizoma, which may cause kidney injury [9]. Perhaps it is the above toxicity of herb that prohibits the import or clinical application of these six decoctions in Sweden, Singapore, the United States, Canada and other countries [10].
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".