Refine
Has Fulltext
- no (2)
Year of publication
- 2020 (2) (remove)
Document Type
- Article (2) (remove)
Language
- English (2)
Is part of the Bibliography
- yes (2)
Keywords
- systematic review (2) (remove)
Institute
Research synthesis on simple yet general hypotheses and ideas is challenging in scientific disciplines studying highly context-dependent systems such as medical, social, and biological sciences. This study shows that machine learning, equation-free statistical modeling of artificial intelligence, is a promising synthesis tool for discovering novel patterns and the source of controversy in a general hypothesis. We apply a decision tree algorithm, assuming that evidence from various contexts can be adequately integrated in a hierarchically nested structure. As a case study, we analyzed 163 articles that studied a prominent hypothesis in invasion biology, the enemy release hypothesis. We explored if any of the nine attributes that classify each study can differentiate conclusions as classification problem. Results corroborated that machine learning can be useful for research synthesis, as the algorithm could detect patterns that had been already focused in previous narrative reviews. Compared with the previous synthesis study that assessed the same evidence collection based on experts' judgement, the algorithm has newly proposed that the studies focusing on Asian regions mostly supported the hypothesis, suggesting that more detailed investigations in these regions can enhance our understanding of the hypothesis. We suggest that machine learning algorithms can be a promising synthesis tool especially where studies (a) reformulate a general hypothesis from different perspectives, (b) use different methods or variables, or (c) report insufficient information for conducting meta-analyses.
This study examined the effectiveness of psychological interventions for severe health anxiety (SHA) regarding somatic symptoms (SS) and health anxiety (HA). The databases Web of Science, EBSCO, and CENTRAL were searched on May 15, 2019, May 16, 2019, and August 5, 2019, respectively. Eighteen randomized controlled trials (N = 2,050) met the inclusion criteria (i.e., hypochondriasis, illness anxiety disorder or somatic symptom disorder with elevated HA being assessed with validated interviews: use of standardized outcome measures). Two reviewers independently evaluated the studies' risk of bias using the Revised Cochrane Risk-of-Bias Tool for randomized trials (RoB-2) tool. Overall, psychological interventions were significantly more effective than waitlist, treatment-as-usual, or placebo post-treatment (g(SS) = 0.70, g(HA) = 1.11) and at follow-up (g(SS) = 0.33, g(HA)= 0.70). CBT outperformed other psychological interventions or pharmacotherapy for HA post- treatment (Hedge's g(HA) = 0.81). The number of sessions did not significantly predict the effect sizes. In sum, psychological interventions were effective for SHA, but the generalizability of the results for SS is limited, because only two high-quatity trials contributed to the comparison.