Xuanfei Baidu decoction, a Chinese herbal medicine for coronavirus disease 2019 (COVID-19): a randomized clinical trial
Bibliographic record
Abstract
Objective: To evaluate the efficacy and safety of Xuanfei Baidu decoction for treating coronavirus disease 2019 (COVID-19). Methods: Patients with COVID-19 were enrolled, and eligible patients were randomly allocated to three groups: group A (Xuanfei Baidu decoction combined with conventional treatment), group B (Ganlu Xiaodu decoction combined with conventional treatment), and group C (conventional treatment only). The duration of treatment was 14 days. The primary outcomes were the duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid testing from positive to negative and hospitalization days. The secondary outcome was the rate of symptom resolution. The safety outcome was drug-related adverse events. Results: In total, 103 patients with ordinary-type COVID-19 were included and randomly allocated to groups A (34 cases), B (35 cases), and C (34 cases). Duration for SARS-CoV-2 nucleic acid testing from positive to negative was shortest in group A [(9.88 ± 3.62) days], followed by groups C [(11.20 ± 2.93) days] and B [(12.69 ± 4.11) days]; differences between the three groups were statistically significant ( P = 0.010). The number of hospitalization days was the least in group A [(14.00 ± 6.55) days], followed by groups B [(15.40 ± 4.02) days] and C [(16.38 ± 5.73) days], and differences between groups were statistically significant ( P = 0.019). There was no statistically significant difference in the rate of symptom resolution between groups ( P > 0.05). No deaths or serious adverse events occurred in either of the groups. Conclusion: Considering the treatment of ordinary-type COVID-19, Xuanfei Baidu decoction can shorten the duration from a positive to negative SARS-CoV-2 nucleic acid test, as well as the duration of hospitalization. Moreover, there were no Xuanfei Baidu decoction-induced adverse reactions. Graphical abstract: http://links.lww.com/AHM/A44.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.002 |
| 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 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".