背景:2015年《巴黎协定》是应对气候变化的第一个限制性协定。新一代的病理学家,他们比他们的前任更担心环境问题,被要求发表研究工作,而他们越来越难,并在人口紧张的背景下。我们想衡量自《巴黎协定》以来病理学生态学研究的兴起。
方法:在十年的研究期间(2013-2022年),我们通过PubMed确定了来自可持续发展词汇的43个术语出现在10个著名的国际病理学期刊上的文章数量,从ScimagoJr中选择他们的SJR指数及其影响因子,再加上《年鉴》,并比较了他们在2013-2015年(m1)和2016-2022年(m2)期间的发病率。同样的过程已经应用于“人工智能”,“深度学习”和“数字病理学”。
结果:共鉴定出1336篇文章。只有“数字病理学”(fromm1=8,33至m2=23,29;p=0,010)和“深度学习”(fromm1=0至m2=10,14;p=0,034)的发病率显着上升。已观察到\“生物学\”的显着下降(从mm1=70,00到m2=56,86;p=0.020)。
结论:病理学对趋势作出反应,但自2015年以来,生态学研究一直处于盲点。然而,作为社论似乎有一种觉醒,国会的文章和通讯在过去两年中蓬勃发展。
BACKGROUND: The 2015 Paris Agreement has been the first restrictive agreement in the fight against climate change. The newer generations of pathologists, who feel more anxiety due to environmental problems than their predecessors, are asked to publish research works while they are harder and harder to and in a context of demographical tensions. We wanted to measure the rise of ecology research in pathology since the Paris Agreement.
METHODS: Over a ten years study period (2013-2022), we have identified via PubMed the number of articles in which forty-three terms taken from the sustainable development vocabulary appeared in ten renowned international pathology journals, selected for their SJR index from ScimagoJr and their impact factor, plus the Annales de pathologie, and compared their means of incidence between the 2013-2015 (m1) and 2016-2022 (m2) periods. The same process has been applied for \"artificial intelligence\", \"deep learning\" and \"digital pathology\".
RESULTS: A total of 1336 articles have been identified. Only \"digital pathology\" (fromm1=8,33 to m2=23,29; p=0,010) and \"deep learning\" (fromm1=0 to m2=10,14; p=0,034) saw their incidence rise significantly. A significant decrease has been observed with \"biological\" (fromm1=70,00 to m2=56,86; p=0,020).
CONCLUSIONS: Pathology reacts to trends but research in ecology has remained in the blind spot since 2015. However there seems to be an awakening as editorials, articles and communications in congress have blossomed the last two years.