Aim: Prediction intervals are a useful measure of uncertainty for meta-analyses that capture the likely effect size of a new (similar) study based on the included studies. This study aimed to: (i) estimate the proportion of meta-analysis studies that report a prediction interval in sports medicine, using medicine as a comparator; and (ii) estimate the proportion of studies with a discrepancy between the reported confidence interval and a calculated prediction interval.Methods: We identified all meta-analysis published between 2012 and 2022 in 10 highly ranked sports medicine and eight medical journals. From these articles, 750 were randomly selected from each discipline. Articles that used a random effect meta-analysis model were included. We randomly selected one meta-analysis from each article to extract data from, which included the number of estimates, the pooled effect, and the confidence and prediction interval.Results: Of the 1500 articles screened, 866 (514 from sports medicine) used a random effect model. The probability of a prediction interval being reported in sports medicine was 1.7% (95% CI = 0.9%, 3.3%). In medicine the probability was 3.9% (95% CI = 2.4%, 6.6%). There were 57% lower odds of a prediction interval being reported in sports medicine (odds ratio = 0.432, 95% CI = 0.178, 0.995). However, the 95% CI was compatible with the difference being practically none. Three of the nine sports medicine studies that reported a prediction interval considered it in their conclusions. A prediction interval was able to be calculated for 220 sports medicine studies. For 60% of these studies, there was a discrepancy in study findings between the reported confidence interval and the calculated prediction interval. Prediction intervals were 3.4 times wider than confidence intervals.Conclusion: Very few meta-analyses report prediction intervals and hence are prone to missing the impact of between-study heterogeneity on the overall conclusions. The widespread misinterpretation of random effect meta-analyses could mean that potentially harmful treatments, or those lacking a sufficient evidence base, are being used in practice. Authors, reviewers, and editors should be aware of the importance of prediction intervals. Journals should consider mandating the inclusion of prediction intervals. Changing the default settings of meta-analysis software to include a prediction interval and changes to the PRISMA reporting guidelines could help improve reporting rates.