During the in vitro fertilization treatment, human chorionic gonadotrophin (hCG) is routinely used as a substitute for the natural endogenous LH surge during the final stage of oocyte maturation. However, it does not provide the FSH surge observed in the mid‐cycle of the natural cycle. To date, whether the FSH surge can improve oocyte quality and pregnancy outcomes remains unknown. Randomized controlled trials comparing the following four trigger methods to conventional hCG were examined: GnRH agonist (GnRHa), kisspeptin, GnRHa plus hCG (dual trigger), and FSH plus hCG (FSH co‐trigger). The results showed that the use of dual triggers was associated with a significantly higher number of retrieved cumulus‐oocyte complexes (COCs) (weighted mean difference [WMD] 1.625, 95% CI 0.684‐2.565), retrieved mature oocytes (WMD 0.986, 95% CI 0.426‐1.545) and fertilized (2PN) oocytes (WMD 0.792, 95% CI 0.083‐1.501), compared with the use of hCG. However, there was no significant difference between the two groups in terms of pregnancy rate. The FSH co‐trigger resulted in significantly higher rates of 2PN oocytes retrieved than the hCG trigger (WMD 0.077, 95% CI 0.028‐0.126). Notably, the risk of OHSS did not differ among the three treatment groups compared to that of the hCG group. This review protocol was registered with PROSPERO (CRD 42020194201).

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While COVID-19 has impacted humans for a long time, people search the web for pandemic-related information, causing anxiety. From a theoretic perspective, previous studies have confirmed that the number of COVID-19 cases can cause negative emotions, but how statistics of different dimensions, such as the number of imported cases, the number of local cases, and the number of governmentdesignated lockdown zones, stimulate people's emotions requires detailed understanding. In order to obtain the views of people on COVID-19, this paper first proposes a deep learning model which classifies texts related to the pandemic from text data with place labels. Next, it conducts a sentiment analysis based on multi-task learning. Finally, it carries out a fixed-effect panel regression with outputs of the sentiment analysis. The performance of the algorithm shows a promising result. The empirical study demonstrates while the number of local cases is positively associated with risk perception, the number of imported cases is negatively associated with confidence levels, which explains why citizens tend to ascribe the protracted pandemic to foreign factors. Besides, this study finds that previous pandemic hits cities recover slowly from the suffering, while local governments' spending on healthcare can improve the situation. Our study illustrates the reasons for risk perception and confidence based on different sources of statistical information due to cognitive bias. It complements the knowledge related to epidemic information. It also contributes to a framework that combines sentiment analysis using advanced deep learning technology with the empirical regression method.