Chinese herbal medicine and COVID-19: quality evaluation of clinical guidelines and expert consensus and analysis of key recommendations
Bibliographic record
Abstract
To systematically review the clinical practice guidelines (CPGs) for the treatment of patients with coronavirus disease 2019 (COVID-19) using Chinese herbal medicine (CHM), assess the methodological quality as well as clinical credibility and implementability of specific recommendations, and summarize key recommendations. Methods: As of April 2022, we conducted a comprehensive search on major electronic databases, guideline websites, academic society websites, and government websites to assess the methodological quality and clinical applicability of the included CPGs using the Appraisal of Guidelines for Research and Evaluation (AGREE) II tool and Evaluation-Recommendations EXcellence (AGREE-REX) instructions, respectively. Results: The search yielded 61 CPGs, which were mostly published in 2020; moreover, 98.4% of the CPGs were published in China. Only five CPGs achieved a high-quality AGREE II rating; further, six CPGs could be directly recommended, with most of the CPGs still showing much room for improvement. CPGs had a low overall score in the AGREE-REX evaluation, with the domains of clinical applicability, values and preferences, and implementability being standardized in 21.80% ± 12.56%, 16.00% ± 11.81%, and 31.33% ± 14.55% of the CPGs, respectively. Five high-quality CPGs mentioned 56 Chinese herbal formulas. Half of the recommendations had moderate or strong evidence level in the GRADE evaluation. The most frequently recommended herbal medicines were Lianhua Qingwen granule/capsule and Jinhua Qinggan granule; however, the strength of recommendation for each prescription varied across CPGs and populations. Conclusions: The overall quality of current CPGs for COVID-19 for CHM still needs to be improved; moreover, the strength of the evidence remains to be standardized across CPGs. Graphical abstract: http://links.lww.com/AHM/A34.
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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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".