Traditional, complementary and integrative medicine therapies for the treatment of mild/moderate acute COVID-19: protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Increasing evidence has emerged for traditional, complementary and integrative medicine (TCIM) to treat COVID-19 which requires systematic summaries of the net benefits of interventions against standard care and one another. The study aims to conduct a systematic review and network meta-analysis (NMA) regarding TCIM therapies for treating mild/moderate acute COVID-19, potentially informing the WHO guideline development and clinical decision-making. METHODS AND ANALYSIS: We will search Embase, MEDLINE, Virtual Health Library on Traditional Complementary and Integrative Medicine, Cochrane Central Register of Controlled Trials, WHO's International Clinical Trials Registry Platform with additional searches of Chinese and Korean literature sources for randomised controlled trials comparing TCIM with placebo, standard care, no treatment or with an alternate type of TCIM to treat COVID-19. We will limit the search dates from 2020 to 28 March 2024. Reviewers will, in duplicate, screen eligible articles, extract data and evaluate the risk of bias. We will conduct frequentist network meta-analyses for network comparisons and each outcome. We will assess the certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation approach and classify interventions from most to least effective or harmful. ETHICS AND DISSEMINATION: This NMA is based on the existing trials and data which is not subject to ethical approval. We will publish the NMA in a peer-reviewed journal. This may provide methodological reference and clinical evidence for TCIM on future epidemic-prone diseases. PROSPERO REGISTRATION NUMBER: CRD42024517321.
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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 | 0.080 | 0.108 |
| Meta-epidemiology (narrow) | 0.008 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.030 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.063 | 0.008 |
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".