208
Views
5
CrossRef citations to date
0
Altmetric
Scientific Section

Applicability of the Tanaka–Johnston and Moyers mixed dentition analyses in Northeast Han Chinese

, , &
Pages 95-102 | Received 17 May 2014, Accepted 21 Oct 2014, Published online: 14 Jan 2015
 

Abstract

Objectives:

To assess applicability of the Tanaka–Johnston and Moyers prediction methods in a Han ethnic group from Northeast China and to develop prediction equations for this same population. Design: Cross-sectional study. Setting: Department of Orthodontics, School of Stomatology, Jiamusi University, Heilongjiang, China. Participants: A total of 130 subjects (65 male and 65 female) aged 16–21 years from a Han ethnic group of Northeast China were recruited from dental students and patients seeking orthodontic treatment. Ethnicity was verified by questionnaire. Material and methods: Mesio-distal tooth width was measured using Digital Vernier calipers. Predicted values were obtained from the Tanaka–Johnston and Moyers methods in both arches were compared with the actual measured widths. Based on regression analysis, prediction equations were developed. Results: Tanaka–Johnston equations were not precise, except for the upper arch in males. However, the Moyers 85th percentile in the upper arch and 75th percentile in the lower arch predicted the sum precisely in males. For females, the Moyers 75th percentile predicted the sum precisely for the upper arch, but none of the Moyers percentiles predicted in the lower arch. Conclusions: Both the Tanaka–Johnston and Moyers method may not be applied universally without question. Hence, it may be safer to develop regression equations for specific populations. Validating studies must be conducted to confirm the precision of these newly developed regression equations.

Log in via your institution

Log in to Taylor & Francis Online

There are no offers available at the current time.

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.