Tehran University Medical Journal

Background: Gender differences in scientometric indicators among medical faculty members in Iran was investigated.
Methods: The Research performance of the faculty at all universities and institutes of medical sciences in Iran was assessed using the Iranian Scientometric Information Database (ISID) on June 12, 2021. Selected variables in our study were name, gender, university, degree, academic rank, type of faculty, the total number of articles, the total number of citations, self-citation percentage, h-index, citation per article, international cooperation percentage, and the number of research collaborators. The Kruskal-Wallis test was used. The extracted data were analyzed using R v4.0.1 statistical software.
Results: A total of 21064 faculty members in 77 universities and institutes of medical sciences were registered in the ISID database, of which 12093 (57.4%) were men. Men faculty members outnumbered women in all academic ranks, except for the “instructor” rank (1134 female instructors against 835 male). In both sexes, most faculty members were assistant professors. There were more articles (346837 vs. 146024) and citations (5177060 vs. 1639246) by men than women. Among the 1789 faculty members with zero articles, 902 (50.4%) were men. One-hundred-and-fifty-four people were among ESI's top researchers' list, with a majority of men (124 people equal to 80.5%). The medians of all the scientometric indicators were higher in men than women. Men had a higher number of articles, the number of citations, h-index, citation per article, percentage of international articles, and co-authors, however, women had lower self-citations (1.56% vs. 2.51%). In all academic ranks, men had higher scientometric indicators. The only exception was the associate professors’ self-citation, in which women’s citations were higher than that of men (3.5 vs. 3.3). The highest mean h-index was in men with a Ph.D. in Pharmacy (13), men with a Ph.D. by Research (12) and women with a Ph.D. by Research (8.5), respectively.
Conclusion: Gender differences were evident in research performance in Iran. Women faculty members of medical sciences in Iran generally had lower scientometric indicators.

Background: Mendelian randomization (MR) is a new generation in the statistical method that uses genetic variants as instrumental variables in data from non-experimental studies to evaluate and estimate the causal effects of risk factors.
Methods: The weakness of observational studies to detect causality, the difficulties of conducting clinical trials, the dramatic advancement of Genome-Wide Association Studies (GWAS) have led to the emergence of a new type of study called MR. It is increasingly being used to determine causality MR is an approach based on meta-analysis methods. The main idea of the MR is based on using the instrument variable (IV) to find the causality between exposure and outcome. This variable does not need to adjust the confounding effects found in observational studies.

Results: Data for this study were collected from the beginning of January 2003 to October 2020 in PubMed. Our results showed that MR has an increasing trend. The data used in MR includes summarized statistical data, individual-level data, and meta-analysis. Choosing the suitable IV is essential to successfully conduct an MR. For an unbiased estimate, three main hypotheses should be considered: 1) The IV has a strong relationship with the desired exposure (i.e., potential risk factor), 2) The IV is not related to the confounding variable, and 3) The IV is not directly related to the outcome and should only relate to the outcome through exposure. If these conditions are not met, one solution is to use robust methods. Besides, this research introduced the study designs, estimation methods, limitations, software packages, and some applications of MR in medical research. Conclusion: When we seek to find a causal relationship, but it is not possible to use a clinical trial as a standard method, the MR design can be used in observational studies. Therefore, it is possible to obtain causal relationships between exposure and outcome using the MR.