Early childhood caries (ECC) remains the most prevalent chronic disease, with a significant impact not only on the quality of life but also on society. 1,2 It is defined as 'the presence of one or more decayed (non-cavitated or cavitated lesions), missing (due to caries), or filled tooth surfaces in any primary tooth in a child under the age of six. In children younger than three years of age, any sign of smooth-surface caries is indicative of severe ECC. 3 Considering the detrimental effects of ECC on both oral and general health, a significant amount of research has been published in the literature. The scientific publications have grown progressively in terms of both the number of journals and their content, doubling the number of published articles in recent years. This makes it cumbersome for budding

Objective The steady and continued increase of the Journal of Oral Pathology & Medicine's (JOPM) popularity prompted a bibliometric analysis of the journal. The purpose was to assess the significance and effect of the published research articles in the Journal from 1972 and 2020, aiding the identification of landmark articles. We performed a bibliometric analysis using the top 100 cited papers in the Journal of Oral Pathology & Medicine. Materials and Methods An extensive review of the Web of Science was undertaken. Standard information such as author details, affiliated institutions, publication year and the country of origin was recorded. Results The top 100 cited articles in JOPM were assessed. The maximum and minimum number of citations in the top 100 articles was 1459 and 95, respectively. A total of 16 790 citations were recorded for these 100 articles. Authors were affiliated to 28 different countries, 17 research articles from the UK and 12 from the USA. Other countries furnished seven or fewer articles. Conclusion This bibliometric analysis provides a synopsis of research published in the journal over a 48‐year period. Recent interest in the journal shows a healthy increase in submissions and profile.

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This article investigates a survival analysis under randomly censored mortality distribution. From the perspective of frequentist, we derive the point estimations through the method of maximum likelihood estimation. Furthermore, approximate confidence intervals for the parameters are constructed based on the asymptotic distribution of the maximum likelihood estimators. Besides, two parametric bootstraps are implemented to construct the approximate confidence intervals for the unknown parameters. In Bayesian framework, the Bayes estimates of the unknown parameters are evaluated by applying the Markov chain Monte Carlo technique, and highest posterior density credible intervals are also carried out. In addition, the Bayes inference based on symmetric and asymmetric loss functions is obtained. Finally, Monte Carlo simulation is performed to observe the behavior of the proposed methods, and a real data set of COVID-19 mortality rate is analyzed for illustration.