Sudden gains have been shown to be a common phenomenon and an important predictor of outcome in psychotherapy (Tang & DeRubeis, 1999). The present meta-analysis examined the growing literature on sudden gains in psychotherapy, their effects on outcome, and moderators of these effects. We searched PsycINFO, PubMed, ProQuest Dissertations & Theses, Scholar and Web of Science databases for articles between January 1st 1999 and May 31st 2019 and included 50 studies (n = 6355) in the meta-analysis. Sudden gains were found to significantly predict outcome at post-treatment (g = 0.68) and follow up (g = 0.61) above and beyond treatments, disorders and settings. Studies using Tang and DeRubies's (1999) criteria for identification of sudden gains had smaller effect sizes compared to studies using altered criteria. Pretreatment severity levels did not significantly predict the effects of sudden gains and neither did treatment setting (randomized controlled trials vs. naturalistic settings). Finally, number of sessions and sudden gains' reversal rates were both negatively associated with the effect sizes of sudden gains. These findings suggest that sudden gains are a ubiquitous phenomenon in psychotherapy but their effects may be moderated by a number of factors. Research and clinical implications are discussed.

A meta-analysis of sudden gains in psychotherapy: Outcome and moderators.

Aggregation across multiple networks highlights robust co-expression interactions and improves the functional connectivity of grapevine gene co-expression networks. In recent years, the rapid accumulation of transcriptome datasets from diverse experimental conditions has enabled the widespread use of gene co-expression network (GCN) analysis in plants. In grapevine, GCN analysis has shown great promise for gene function prediction, however, measurable progress is currently lacking. Using accumulated microarray datasets from the grapevine whole-genome array (33 experiments, 1359 samples), we explored how meta-analysis through aggregation influences the functional connectivity (performance) of derived networks using guilt-by-association neighbor voting. Two annotation schemes, i.e. MapMan BIN and Pfam, at two sparsity thresholds, i.e. top 100 (stringent) and 300 (relaxed) ranked genes were evaluated. We observed that aggregating across multiple networks improves performance dramatically, with the aggregate outperforming the majority of functional terms across individual networks. Network sparsity and size (i.e. the number of samples and aggregates) were key factors influencing performance while the choice of annotation scheme had little. Systematic comparison with various state-of-the-art microarray and RNA-seq networks was also performed, however, none outperformed the aggregate microarray network despite having good predictive performance. Repeating these series of tests using a functional enrichment-based performance metric also showed remarkably consistent findings with guilt-by-association neighbor voting. To demonstrate its functionality, we explore the function and transcriptional regulation of grapevine EXPANSIN genes. We envisage that network aggregation will offer new and unique opportunities for gene function prediction in future grapevine functional genomics studies. To this end, we make the aggregate networks and associated metadata publicly available at VTC-Agg (https://sites.google.com/view/vtc-agg).

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