Among the massive randomized controlled trials(RCTs) regarding acupuncture treatment of depression，Hamilton Depression Rating Scale(HAM-D) is most frequently used to evaluate the severity of depression.This study aimed to determine the integrity of the reported HAM-D results，especially the researchers' attention to the interpretation of the clinical significance of the results.Methods:RCTs on acupuncture treatment of depression and with reported HAM-D results published on 9 databases ［China National Knowledge Infrastructure(CNKI)，China Science Periodical Database(CSPD)，Chinese Citation Database(CCD)，Chinese Clinical Trial Register(ChiCTR)，PubMed，Cochrane，Embase，Web of Science，and ProQuest］ from October 2020 to October 2022 were selected.Two researchers independently extracted the details of HAM-D scores in each RCT and the clinical significance of the results.Results:A total of 91 RCTs meeting the criteria were included in this review.Among them，more than 95% reported the mean，standard deviation and P value，while only 3.3% reported the 95% confidence interval; 93.4% directly or indirectly studied the meaning of HAMD values; 73.6% discussed the classification of HAM-D; 19.8% reported the total HAM-D score; 34.1% elucidated the specific evaluation method of HAM-D.Statistical significance was found in more than 75% of the RCTs.In addition，all included RCTs interpreted the clinical significance，but the interpretation was only made based on statistical significance，and failed to combine the actual clinical effective values.Inter-group comparison showed that the percentage of each item reported in the dissertations was higher than that in the general literature.And foreign reports had higher integrity than those in China.Conclusion:The HAM-D results in Chinese RCTs on acupuncture treatment of depression need to be improved in integrity.It is suggested that future studies should consult high-quality RCT results，describe the scoring indexes in detail，and reasonably interpret the clinical significance of the results，to improve the quality of outcome indexes.