RESUMEN
BACKGROUND: The overall comprehensive consideration of the factors influencing the recommendations in the traditional Chinese medicine (TCM) guidelines remains poorly studied. This study systematically evaluate the factors influencing recommendations formation in the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) clinical practice guidelines (CPGs) and TCM CPGs. METHODS: This was a methodological review in which we searched six databases and multiple related websites. The GRADE CPGs were identified as the guidelines developed by the GRADE Working Group or the two Co-Chairs. For the TCM CPGs, we randomly selected guidelines that were published by the TCM or integrative medicine academic societies from China mainland (published by the TCM or integrative medicine academic societies of China mainland). Two reviewers independently screened and extracted data. We included CPGs published in 2018-2022. We extracted information on the influencing factors of evidence to recommendation and conducted the analyses using descriptive statistics and calculated the proportion of relevant items by IBM SPSS Statistics and Microsoft Excel to compare the differences between the GRADE CPGs and the TCM CPGs. RESULTS: Forty-five GRADE CPGs (including 912 recommendations) and 88 TCM CPGs (including 2452 recommendations) were included. TCM recommendations mainly considered the four key determinants of desirable anticipated effects, undesirable anticipated effects, balance between desirable and undesirable effects, certainty of evidence, with less than 20% of other dimensions. And TCM CPGs presented more strong recommendations (for or against) and inappropriate discordant recommendations than GRADE CPGs. GRADE CPGs were more comprehensive considered about the factors affecting the recommendations, and considered more than 70% of all factors in the evidence to recommendation. CONCLUSIONS: The TCM CPGs lack a comprehensive consideration of multiple influencing determinants from evidence to recommendations. In the future, the correct application of the GRADE approaches should be emphasized.