PDF(Pubmed)
Abstract：
BACKGROUND: Smell disorders are commonly reported with COVID-19 infection. The smell-related issues associated with COVID-19 may be prolonged, even after the respiratory symptoms are resolved. These smell dysfunctions can range from anosmia (complete loss of smell) or hyposmia (reduced sense of smell) to parosmia (smells perceived differently) or phantosmia (smells perceived without an odor source being present). Similar to the difficulty that people experience when talking about their smell experiences, patients find it difficult to express or label the symptoms they experience, thereby complicating diagnosis. The complexity of these symptoms can be an additional burden for patients and health care providers and thus needs further investigation.
OBJECTIVE: This study aims to explore the smell disorder concerns of patients and to provide an overview for each specific smell disorder by using the longitudinal survey conducted in 2020 by the Global Consortium for Chemosensory Research, an international research group that has been created ad hoc for studying chemosensory dysfunctions. We aimed to extend the existing knowledge on smell disorders related to COVID-19 by analyzing a large data set of self-reported descriptive comments by using methods from natural language processing.
METHODS: We included self-reported data on the description of changes in smell provided by 1560 participants at 2 timepoints (second survey completed between 23 and 291 days). Text data from participants who still had smell disorders at the second timepoint (long-haulers) were compared with the text data of those who did not (non-long-haulers). Specifically, 3 aims were pursued in this study. The first aim was to classify smell disorders based on the participants\' self-reports. The second aim was to classify the sentiment of each self-report by using a machine learning approach, and the third aim was to find particular food and nonfood keywords that were more salient among long-haulers than those among non-long-haulers.
RESULTS: We found that parosmia (odds ratio [OR] 1.78, 95% CI 1.35-2.37; P<.001) as well as hyposmia (OR 1.74, 95% CI 1.34-2.26; P<.001) were more frequently reported in long-haulers than in non-long-haulers. Furthermore, a significant relationship was found between long-hauler status and sentiment of self-report (P<.001). Finally, we found specific keywords that were more typical for long-haulers than those for non-long-haulers, for example, fire, gas, wine, and vinegar.
CONCLUSIONS: Our work shows consistent findings with those of previous studies, which indicate that self-reports, which can easily be extracted online, may offer valuable information to health care and understanding of smell disorders. At the same time, our study on self-reports provides new insights for future studies investigating smell disorders.
OBJECTIVE: This study aims to explore the smell disorder concerns of patients and to provide an overview for each specific smell disorder by using the longitudinal survey conducted in 2020 by the Global Consortium for Chemosensory Research, an international research group that has been created ad hoc for studying chemosensory dysfunctions. We aimed to extend the existing knowledge on smell disorders related to COVID-19 by analyzing a large data set of self-reported descriptive comments by using methods from natural language processing.
METHODS: We included self-reported data on the description of changes in smell provided by 1560 participants at 2 timepoints (second survey completed between 23 and 291 days). Text data from participants who still had smell disorders at the second timepoint (long-haulers) were compared with the text data of those who did not (non-long-haulers). Specifically, 3 aims were pursued in this study. The first aim was to classify smell disorders based on the participants\' self-reports. The second aim was to classify the sentiment of each self-report by using a machine learning approach, and the third aim was to find particular food and nonfood keywords that were more salient among long-haulers than those among non-long-haulers.
RESULTS: We found that parosmia (odds ratio [OR] 1.78, 95% CI 1.35-2.37; P<.001) as well as hyposmia (OR 1.74, 95% CI 1.34-2.26; P<.001) were more frequently reported in long-haulers than in non-long-haulers. Furthermore, a significant relationship was found between long-hauler status and sentiment of self-report (P<.001). Finally, we found specific keywords that were more typical for long-haulers than those for non-long-haulers, for example, fire, gas, wine, and vinegar.
CONCLUSIONS: Our work shows consistent findings with those of previous studies, which indicate that self-reports, which can easily be extracted online, may offer valuable information to health care and understanding of smell disorders. At the same time, our study on self-reports provides new insights for future studies investigating smell disorders.
摘要：
背景：据报道，COVID-19感染常伴有嗅觉障碍。与COVID-19相关的气味问题可能会延长，即使在呼吸道症状解决之后。这些嗅觉功能障碍的范围可以从嗅觉缺失(完全丧失嗅觉)或嗅觉减退(嗅觉减少)到视障(感觉不同)或幻影(感觉到气味没有气味源存在)。类似于人们在谈论嗅觉体验时遇到的困难，患者发现很难表达或标记他们经历的症状，从而使诊断复杂化。这些症状的复杂性可能会给患者和医疗保健提供者带来额外负担，因此需要进一步调查。
目的：本研究旨在通过使用全球化学感官研究联盟在2020年进行的纵向调查，探索患者对嗅觉障碍的关注，并为每种特定的嗅觉障碍提供概述。一个专门为研究化学感觉功能障碍而成立的国际研究小组。我们旨在通过使用自然语言处理的方法分析大量自我报告的描述性评论数据集，从而扩展与COVID-19相关的嗅觉障碍的现有知识。
方法：我们包括了1560名参与者在2个时间点提供的关于气味变化描述的自我报告数据（第二次调查在23至291天之间完成）。将在第二个时间点仍有嗅觉障碍的参与者（长途运输者）的文本数据与没有嗅觉障碍的参与者（非长途运输者）的文本数据进行比较。具体来说,本研究追求3个目标。第一个目的是根据参与者的自我报告对嗅觉障碍进行分类。第二个目的是使用机器学习方法对每个自我报告的情绪进行分类，第三个目标是找到特定的食物和非食物关键词，这些关键词在长途运输者中比非长途运输者中更突出。
结果：我们发现，假性（比值比[OR]1.78，95％CI1.35-2.37；P<.001）以及低症（OR1.74，95％CI1.34-2.26；P<.001）在长途运输者中比非长途运输者中更常见。此外，发现长途运输状态与自我报告情绪之间存在显着关系（P<.001）。最后,我们发现特定的关键词对于长途运输者比非长途运输者更典型，例如,火,气体,葡萄酒,还有醋.
结论：我们的研究结果与以前的研究结果一致，这表明自我报告，可以很容易地在线提取，可能为医疗保健和理解嗅觉障碍提供有价值的信息。同时,我们对自我报告的研究为未来研究嗅觉障碍提供了新的见解.
目的：本研究旨在通过使用全球化学感官研究联盟在2020年进行的纵向调查，探索患者对嗅觉障碍的关注，并为每种特定的嗅觉障碍提供概述。一个专门为研究化学感觉功能障碍而成立的国际研究小组。我们旨在通过使用自然语言处理的方法分析大量自我报告的描述性评论数据集，从而扩展与COVID-19相关的嗅觉障碍的现有知识。
方法：我们包括了1560名参与者在2个时间点提供的关于气味变化描述的自我报告数据（第二次调查在23至291天之间完成）。将在第二个时间点仍有嗅觉障碍的参与者（长途运输者）的文本数据与没有嗅觉障碍的参与者（非长途运输者）的文本数据进行比较。具体来说,本研究追求3个目标。第一个目的是根据参与者的自我报告对嗅觉障碍进行分类。第二个目的是使用机器学习方法对每个自我报告的情绪进行分类，第三个目标是找到特定的食物和非食物关键词，这些关键词在长途运输者中比非长途运输者中更突出。
结果：我们发现，假性（比值比[OR]1.78，95％CI1.35-2.37；P<.001）以及低症（OR1.74，95％CI1.34-2.26；P<.001）在长途运输者中比非长途运输者中更常见。此外，发现长途运输状态与自我报告情绪之间存在显着关系（P<.001）。最后,我们发现特定的关键词对于长途运输者比非长途运输者更典型，例如,火,气体,葡萄酒,还有醋.
结论：我们的研究结果与以前的研究结果一致，这表明自我报告，可以很容易地在线提取，可能为医疗保健和理解嗅觉障碍提供有价值的信息。同时,我们对自我报告的研究为未来研究嗅觉障碍提供了新的见解.
