Author-paper affiliation network architecture influences the methodological quality of systematic reviews and meta-analyses of psoriasis

<div><p>Moderate-to-severe psoriasis is associated with significant comorbidity, an impaired quality of life, and increased medical costs, including those associated with treatments. Systematic reviews (SRs) and meta-analyses (MAs) of randomized clinical trials are considered two of the best approaches to the summarization of high-quality evidence. However, methodological bias can reduce the validity of conclusions from these types of studies and subsequently impair the quality of decision making. As co-authorship is among the most well-documented forms of research collaboration, the present study aimed to explore whether authors’ collaboration methods might influence the methodological quality of SRs and MAs of psoriasis. Methodological quality was assessed by two raters who extracted information from full articles. After calculating total and per-item Assessment of Multiple Systematic Reviews (AMSTAR) scores, reviews were classified as low (0-4), medium (5-8), or high (9-11) quality. Article metadata and journal-related bibliometric indices were also obtained. A total of 741 authors from 520 different institutions and 32 countries published 220 reviews that were classified as high (17.2%), moderate (55%), or low (27.7%) methodological quality. The high methodological quality subnetwork was larger but had a lower connection density than the low and moderate methodological quality subnetworks; specifically, the former contained relatively fewer nodes (authors and reviews), reviews by authors, and collaborators per author. Furthermore, the high methodological quality subnetwork was highly compartmentalized, with several modules representing few poorly interconnected communities. In conclusion, structural differences in author-paper affiliation network may influence the methodological quality of SRs and MAs on psoriasis. As the author-paper affiliation network structure affects study quality in this research field, authors who maintain an appropriate balance between scientific quality and productivity are more likely to develop higher quality reviews.</p></div

The differential impact of scientific quality, bibliometric factors, and social media activity on the influence of systematic reviews and meta-analyses about psoriasis

<div><p>Researchers are increasingly using on line social networks to promote their work. Some authors have suggested that measuring social media activity can predict the impact of a primary study (i.e., whether or not an article will be highly cited). However, the influence of variables such as scientific quality, research disclosures, and journal characteristics on systematic reviews and meta-analyses has not yet been assessed. The present study aims to describe the effect of complex interactions between bibliometric factors and social media activity on the impact of systematic reviews and meta-analyses about psoriasis (PROSPERO 2016: CRD42016053181). Methodological quality was assessed using the Assessing the Methodological Quality of Systematic Reviews (AMSTAR) tool. Altmetrics, which consider Twitter, Facebook, and Google+ mention counts as well as Mendeley and SCOPUS readers, and corresponding article citation counts from Google Scholar were obtained for each article. Metadata and journal-related bibliometric indices were also obtained. One-hundred and sixty-four reviews with available altmetrics information were included in the final multifactorial analysis, which showed that social media and impact factor have less effect than Mendeley and SCOPUS readers on the number of cites that appear in Google Scholar. Although a journal’s impact factor predicted the number of tweets (OR, 1.202; 95% CI, 1.087–1.049), the years of publication and the number of Mendeley readers predicted the number of citations in Google Scholar (OR, 1.033; 95% CI, 1.018–1.329). Finally, methodological quality was related neither with bibliometric influence nor social media activity for systematic reviews. In conclusion, there seems to be a lack of connectivity between scientific quality, social media activity, and article usage, thus predicting scientific success based on these variables may be inappropriate in the particular case of systematic reviews.</p></div

Summary statistics for the co-authorship networks based on methodology quality.

<p>Summary statistics for the co-authorship networks based on methodology quality.</p

Cumulative frequency of node degree distributions.

<p>The cumulative node degree distribution shows the cumulative number of nodes of each degree class. The distribution of degree values over all nodes characterizes the network as a whole.</p

Influence of authors’ scientific quality and productivity on methodological quality of SRs and MAs about psoriasis.

<p>Panel (a-c): Bubble plot that represents the number of publications by author. Bubble size is proportional to the author’s H-index. Authors are sorted by their institution’s country. Panel (d-f) represents a scatter plot of author’s H-index vs. number of authored publications. Smoothed fitted lines represent predictions using linear regression for every country. Points and lines are colored based on author institution country.</p

Whole author-paper affiliation network.

<p>(a). Nodes that represent authors are colored and labeled based on their institution’s country. Grey nodes represent the reviews on psoriasis which were finally included. Node size is proportional to the author’s H-index or AMSTAR score respectively. Edges connect both types of nodes, thus every author and their collaborators are linked to the shared publication. (b). Same network, although only nodes representing articles are colored based on AMSTAR levels.</p

Author-paper affiliation subnetworks based on methodological quality of the reviews.

<p>Nodes that represent authors are colored and labeled based on their institution’s country. Grey nodes represent the SRs and MAs on psoriasis which were finally included. Node size is proportional to the author’s H-index or AMSTAR score respectively. Edges connect both types of nodes, thus every author and their collaborators are connected to the shared publication.</p

Gilberte. Bouffes. Mam'zelle Crénom : [photographie, tirage de démonstration] / [Atelier Nadar]

Appendix 5. Phase 2 domains and signaling questions of ROBIS tool. (DOC 32 kb