The Quality of Methods Reporting in Parasitology Experiments

Abstract

There is a growing concern both inside and outside the scientific community over the lack of reproducibility of experiments. The depth and detail of reported methods are critical to the reproducibility of findings, but also for making it possible to compare and integrate data from different studies. In this study, we evaluated in detail the methods reporting in a comprehensive set of trypanosomiasis experiments that should enable valid reproduction, integration and comparison of research findings. We evaluated a subset of other parasitic (Leishmania, Toxoplasma, Plasmodium, Trichuris and Schistosoma) and non-parasitic (Mycobacterium) experimental infections in order to compare the quality of method reporting more generally. A systematic review using PubMed (2000-2012) of all publications describing gene expression in cells and animals infected with Trypanosoma spp was undertaken based on PRISMA guidelines; 23 papers were identified and included. We defined a checklist of essential parameters that should be reported and have scored the number of those parameters that are reported for each publication. Bibliometric parameters (impact factor, citations and h-index) were used to look for association between Journal and Author status and the quality of method reporting. Trichuriasis experiments achieved the highest scores and included the only paper to score 100% in all criteria. The mean of scores achieved by Trypanosoma articles through the checklist was 65.5% (range 32-90%). Bibliometric parameters were not correlated with the quality of method reporting (Spearman's rank correlation coefficient <-0.5; p>0.05). Our results indicate that the quality of methods reporting in experimental parasitology is a cause for concern and it has not improved over time, despite there being evidence that most of the assessed parameters do influence the results. We propose that our set of parameters be used as guidelines to improve the quality of the reporting of experimental infection models as a pre-requisite for integrating and comparing sets of data.

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