The Quality of Statistical Reporting and Data Presentation in Predatory Dental Journals Was Lower Than in Non-Predatory Journals

Abstract

Proper peer review and quality of published articles are often regarded as signs of reliable scientific journals. The aim of this study was to compare whether the quality of statistical reporting and data presentation differs among articles published in ‘predatory dental journals’ and in other dental journals. We evaluated 50 articles published in ‘predatory open access (OA) journals’ and 100 clinical trials published in legitimate dental journals between 2019 and 2020. The quality of statistical reporting and data presentation of each paper was assessed on a scale from 0 (poor) to 10 (high). The mean (SD) quality score of the statistical reporting and data presentation was 2.5 (1.4) for the predatory OA journals, 4.8 (1.8) for the legitimate OA journals, and 5.6 (1.8) for the more visible dental journals. The mean values differed significantly (p < 0.001). The quality of statistical reporting of clinical studies published in predatory journals was found to be lower than in open access and highly cited journals. This difference in quality is a wake-up call to consume study results critically. Poor statistical reporting indicates wider general lower quality in publications where the authors and journals are less likely to be critiqued by peer review.

The use of the phrase “data not shown” in dental research

“…Many of the studied PMCOA articles were from open access dental journals, indexed in PubMed (like BMC Oral Health and Clinical and Experimental Dental Research). Nieminen and Uribe [ 33 ] showed that in non-predatory (legitimate and indexed by established databases) open access dental journals, the presentation of results (particularly in tables and figures) was poorer than in more visible subscription-based dental journals but still better than in predatory (non-indexed) dental journals (from predatory publishers). Since referring to “data not shown” evidently is an obscure way of presenting results, it may be related to how results are presented in these studies in general.…”

Statistics in medical research: Common mistakes

“…For example, a study on the relationship between meat consumption and heart disease that fails to account for other factors that may contribute to heart disease, such as smoking or physical activity, or a study on the effectiveness of a new treatment for tuberculosis that fails to account for differences in disease severity or other factors that may influence treatment outcomes. 7 Errors in the application of statistical tests. Choosing the wrong test for a particular dataset affects the validation of observations during research.…”

“…This means that they did not consider the fact that they tested multiple outcomes (blood pressure, heart rate, and cholesterol levels) and that there is a higher chance of obtaining a false positive result (i.e., a result that appears significant but is actually due to chance) when testing multiple outcomes. 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 Inappropriate use of p-values. p-Values are often misinterpreted as a measure of effect size or clinical relevance, when in fact they only indicate the probability of observing the study results by chance.…”

“…For example, a study that conducts multiple analyses on a dataset without a clear hypothesis or theoretical framework and reports a significant association between a particular variable and an outcome, even though the finding may be due to chance. 2 , 3 , 4 , 5 , 6 , 7 Publication bias. This occurs when studies with positive or statistically significant results are more likely to be published than those with negative or non-significant results, thus leading to overestimation of the true effect size.…”

“…This means that they did not consider the fact that they tested multiple outcomes (blood pressure, heart rate, and cholesterol levels) and that there is a higher chance of obtaining a false positive result (i.e., a result that appears significant but is actually due to chance) when testing multiple outcomes. 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 …”