  Topic modeling and text mining are subsets of Natural Language Processing
(NLP) with relevance for conducting meta-analysis (MA) and systematic review
(SR). For evidence synthesis, the above NLP methods are conventionally used for
topic-specific literature searches or extracting values from reports to
automate essential phases of SR and MA. Instead, this work proposes a
comparative topic modeling approach to analyze reports of contradictory results
on the same general research question. Specifically, the objective is to
identify topics exhibiting distinct associations with significant results for
an outcome of interest by ranking them according to their proportional
occurrence in (and consistency of distribution across) reports of significant
effects. The proposed method was tested on broad-scope studies addressing
whether supplemental nutritional compounds significantly benefit macular
degeneration (MD). Four of these were further supported in terms of
effectiveness upon conducting a follow-up literature search for validation
(omega-3 fatty acids, copper, zeaxanthin, and nitrates). The two not supported
by the follow-up literature search (niacin and molybdenum) also had scores in
the lowest range under the proposed scoring system, suggesting that the
proposed methods score for a given topic may be a viable proxy for its degree
of association with the outcome of interest and can be helpful in the search
for potentially causal relationships. These results underpin the proposed
methods potential to add specificity in understanding effects from broad-scope
reports, elucidate topics of interest for future research, and guide evidence
synthesis in a systematic and scalable way. All of this is accomplished while
yielding valuable insights into the prevention of MD.
