Autism Research Paper Abstract

Bibliometric information was extracted for each article, including author(s), affiliation(s), publication title, journal, attributed keywords and cited references.This information was formatted and processed within R software (R Core Team, 2016) using functionality within the ‘tm’ and ‘Bibliometric and Co-citation Analysis’ packages (Aria and Cuccurullo, 2017).The nosology and epidemiology of Autism has undergone transformation following consolidation of once disparate disorders under the umbrella diagnostic, autism spectrum disorders.

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However, modularity and citation analytics demonstrate dominance of subjective psychological or psychiatric constructs, which may impede progress in the identification and stratification of biomarkers as endorsed by new research initiatives.

“There are in fact two things, science and opinion; the former begets knowledge, the latter ignorance” -Hippocrates.

This application of statistics before heuristics marks a departure from traditional hand coding and text-processing techniques.

Underpinned by empirical, self-emergent trends as identified across the broad Autism corpus, these emerging patterns enable discussion as to the past, current and future direction of Autism research, within a broader context of new health initiatives.

To this end, modern bibliometric techniques were empirically informed and coupled with computational metrics, self-clustering network analyses and graph theory visualization methods, to provide accessible summative data of the dynamic evolution and focus of Autism research between 19.

Research focus and trends were assessed at two levels; (1) examination of broad trends in research proliferation, journal focus, and citation analytics through objective time-series analytics, (2) comprehensive empirical co-keyword analysis to delineate self-emerging areas of research convergence via modularity statistics.

A total of 25,782 extracted keywords were pre-processed to trim white space and remove numeric only entries before being converted to lower case and stemmed to characters 1:6 (facilitating the automatic augmentation of similar terms, e.g., Behavior, Behavioral, and Behavioral Intervention were subsumed into a single representative term).

Duplicates were combined creating a unique dictionary of 6242 stemmed keywords for corpus analysis.

This comprehensive report profiled a 12-fold increase in publication rates, with a hand coding methodology (designed a priori to target the dimensions of interest), highlighting the now prominent role of biological and physiological processes in Autism focused research (Office of Autism Research Coordination (OARC) and and National Institute of Mental Health and Thomson Reuters, Inc., 2012).

Yet, while inferring a promising shift in the scope of Autism research, the reliance on hand coding and counting techniques employed in both reviews limit the applicability of such results.

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