There has been a proliferation of descriptive for COVID-19 papers using altmetrics. The main objective of this study is to analyse whether the altmetric mentions of COVID-19 medical studies are associated with the type of study and its level of evidence. Data were collected from PubMed and Altmetric.com databases. A total of 16,672 study types (e.g., Case reports or Clinical trials) published in the year 2021 and with at least one altmetric mention were retrieved. The altmetric indicators considered were Altmetric Attention Score (AAS), News mentions, Twitter mentions, and Mendeley readers. Once the dataset had been created, the first step was to carry out a descriptive study. Then a normality hypothesis was contrasted by means of the Kolmogorov-Smirnov test, and since it was significant in all cases, the overall comparison of groups was performed using the non-parametric Kruskal-Wallis test. When this test rejected the null hypothesis, pair-by-pair comparisons were performed with the Mann-Whitney U test, and the intensity of the possible association was measured using Cramers V coefficient. The results suggest that the data do not fit a normal distribution. The Mann-Whitney U test revealed coincidences in five groups of study types, the altmetric indicator with most coincidences being news mentions and the study types with the most coincidences were the systematic reviews together with the meta-analyses, which coincided with four altmetric indicators. Likewise, between the study types and the altmetric indicators, a weak but significant association was observed through the chi-square and Cramers V. It is concluded that the positive association between altmetrics and study types in medicine could reflect the level of the pyramid of scientific evidence.

翻译：已有大量研究利用替代计量指标对COVID-19相关论文进行描述性分析。本研究的主要目标是分析COVID-19医学研究的替代计量提及次数是否与研究类型及其证据等级相关。数据来源于PubMed和Altmetric.com数据库。共检索到16,672篇发表于2021年且至少有一次替代计量提及的研究类型（如病例报告或临床试验）。所考虑的替代计量指标包括：替代计量关注分数（AAS）、新闻提及次数、Twitter提及次数以及Mendeley读者数。数据集构建完成后，首先进行描述性研究，然后通过Kolmogorov-Smirnov检验对正态性假设进行检验，由于所有案例均显著，因此使用非参数Kruskal-Wallis检验进行组间整体比较。当该检验拒绝原假设时，采用Mann-Whitney U检验进行两两比较，并通过Cramér's V系数衡量可能关联的强度。结果表明数据不符合正态分布。Mann-Whitney U检验显示五组研究类型存在一致性，其中替代计量指标中新闻提及次数的一致性最高，而研究类型中系统评价与荟萃分析的一致性最高，它们与四个替代计量指标均一致。此外，通过卡方检验和Cramér's V系数发现，研究类型与替代计量指标之间存在弱但显著的相关性。研究表明，医学领域中替代计量指标与研究类型之间的正相关关系可能反映了科学证据金字塔的层级。