Use of traditional Chinese medicine for the treatment and prevention of COVID-19 and rehabilitation of COVID-19 patients: An evidence mapping study
Bibliographic record
Abstract
Background: The potential effectiveness of traditional Chinese medicine (TCM) against “epidemic diseases” has highlighted the knowledge gaps associated with TCM in COVID-19 management. This study aimed to map the matrix for rigorously assessing, organizing, and presenting evidence relevant to TCM in COVID-19 management. Methods: In this study, we used the methodology of evidence mapping (EM). Nine electronic databases, the WHO International Clinical Trials Registry Platform (ICTRP) Search Portal, ClinicalTrials.gov , gray literature, reference lists of articles, and relevant Chinese conference proceedings, were searched for articles published until 23 March 2022. The EndNote X9, Rayyan, EPPI, and R software were used for data entry and management. Results: In all, 126 studies, including 76 randomized controlled trials (RCTs) and 50 systematic reviews (SRs), met our inclusion criteria. Of these, only nine studies (7.14%) were designated as high quality: four RCTs were assessed as “low risk of bias” and five SRs as “high quality.” Based on the research objectives of these studies, the included studies were classified into treatment (53 RCTs and 50 SRs, 81.75%), rehabilitation (20 RCTs, 15.87%), and prevention (3 RCTs, 2.38%) groups. A total of 76 RCTs included 59 intervention categories and 57 efficacy outcomes. All relevant trials consistently demonstrated that TCM significantly improved 22 outcomes (i.e., consistent positive outcomes) without significantly affecting four (i.e., consistent negative outcomes). Further, 50 SRs included nine intervention categories and 27 efficacy outcomes, two of which reported consistent positive outcomes and two reported consistent negative outcomes. Moreover, 45 RCTs and 38 SRs investigated adverse events; 39 RCTs and 30 SRs showed no serious adverse events or significant differences between groups. Conclusion: This study provides evidence matrix mapping of TCM against COVID-19, demonstrating the potential efficacy and safety of TCM in the treatment and prevention of COVID-19 and rehabilitation of COVID-19 patients, and also addresses evidence gaps. Given the limited number and poor quality of available studies and potential concerns regarding the applicability of the current clinical evaluation standards to TCM, the effect of specific interventions on individual outcomes needs further evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".