| Literature DB >> 23724204 |
Javad Chalipa1, Mohammad Sadegh Ahmad Akhoundi, Elinaz Shoshtarimoghaddam, Tahereh Hosseinzadeh Nik, Mosle Imani.
Abstract
OBJECTIVES: Cephalometry and its related analyses have an important role in the evaluation of orthodontic patients. Access to an analysis that gives maximum information in the least possible time is an effective way to indicate craniofacial disharmony; therefore, craniofacial templates are very useful tools. The purpose of the present study was to provide orthodontic craniofacial templates for 8-14-year-old Iranian girls.Entities:
Keywords: Analysis; Cephalometry; Malocclusion; Orthodontics
Year: 2013 PMID: 23724204 PMCID: PMC3666066
Source DB: PubMed Journal: J Dent (Tehran) ISSN: 1735-2150
Definitions of the Landmarks Selected for Tracing
| Landmarks | Definitions |
|---|---|
| The constructed point as the center of Sella Turcica | |
| Nasion: The most superior Anterior point of the nasion bone | |
| Orbitale: The Lower-most point of the orbit | |
| Porion: The highest point on the bony external acoustic meatus | |
| Anterior Nasal Spine | |
| Posterior Nasal Spine | |
| Point A: Subspinale | |
| Point B: Supramentale | |
| Pogonion :The most anterior point of bony chin | |
| Menton: The lower most point on the mandibular symphisis | |
| Gonion : The most posterior Inferior point on the angle of the mandible, A constructed | |
| Articulare : A constructed point at the intersection of the images between posterior border of ramus and cranial base | |
| Basion :Lowest point on the anterior margin of the foramen magnum | |
| Incision superius | |
| Incision Inferius | |
| The most mesial contact point of the first molar & it’s mesial tooth | |
| The Anterior border of the pterygopalatine fossa |
First Method Multivariate Regression Analysis to Evaluate the Changes of Cephalometric Vectors Regarding Age
| X | 66.91 + 0.33 (age) | P=0.019 | P=0.019 | |
| Y | 0 | |||
| X | 48.14 + 0.32 (age) | P=0.042 | P=0.011 | |
| Y | −24.09 – 0.23 (age) | P=0.015 | ||
| X | −22.18 – 0.50 (age) | P=0.001 | P=0.003 | |
| Y | −18.57 + 0.18 (age) | P=0.118 | ||
| X | 61.96 + 0.40 (age) | P=0.026 | P=0.0001 | |
| Y | −41.24 – 0.98 (age) | P=0.0001 | ||
| X | 18.39 – 0.31 (age) | P=0.029 | P=0.0001 | |
| Y | −35.04 – 0.82 (age) | P=0.0001 | ||
| X | 56.21 + 0.41 (age) | P=0.029 | P=0.0001 | |
| Y | −46.03 + 1.09 (age) | P=0.0001 | ||
| X | 42.76 + 0.60 (age) | P=0.015 | P=0.0001 | |
| Y | −74.73 – 1.50 (age) | P=0.0001 | ||
| X | 39.68 + 0.71 (age) | P=0.007 | P=0.0001 | |
| Y | −82.48 + 2.03 (age) | P=0.0001 | ||
| X | 32.05 + 0.62 (age) | P=0.020 | P=0.0001 | |
| Y | −86.29 – 2.13 (age) | P=0.0001 | ||
| X | −5.89 – 1.14 (age) | P=0.0001 | P=0.0001 | |
| Y | −55.44 – 1.50 (age) | P=0.0001 | ||
| X | −11.90 – 0.67 (age) | P=0.0001 | P=0.0001 | |
| Y | −23.92 – 0.40 (age) | P=0.0004 | ||
| X | −24.95 – 0.43 (age) | P=0.005 | P=0.008 | |
| Y | −33.46 – 0.18 (age) | P=0.284 | ||
| X | 53.28 + 0.64 (age) | P=0.005 | P=0.0001 | |
| Y | −63.61 – 1.37 (age) | P=0.0001 | ||
| X | 50.41 + 069 (age) | P=0.001 | P=0.0001 | |
| Y | −60.20 – 1.39 (age) | P=0.0001 | ||
| X | 26.68 + 0.68 (age) | P=0.0006 | P=0.0001 | |
| Y | −45.98 + 1.71 (age) | P=0.0001 | ||
| X | 23.54 + 0.14 (age) | P=0.254 | P=0.254 | |
| Y | 0 |
Fig 1Graphic model (Template) derived by the first method
Second Method Multivariate Regression Analysis to Evaluate the Changes of Cephalometric Vectors Regarding Age
| X | −22.20 – 0.16 (age) | P=0.206 | P=0.451 | |
| Y | 11.90 + 0.01 (age) | P=0.951 | ||
| X | 42.26 + 0.07 (age) | P=0648 | P=0.0002 | |
| Y | 25.74 – 0.69 (age) | P=0.0004 | ||
| X | 30.41 + 0.31 (age) | P=0.021 | P=0.001 | |
| Y | −3.51 + 0.47 (age) | P=0.004 | ||
| X | −40.89 – 0.37 (age) | P=0.014 | P=0.025 | |
| Y | −9.93 – 0.26 (age) | P=0.232 | ||
| X | 45.87 – 0.86 (age) | P=0.0001 | P=0.0001 | |
| Y | −16.77 – 0.09 (age) | P=0.627 | ||
| X | 2.27 + 0.18 (age) | P=0.178 | P=0.0001 | |
| Y | −18.67 – 0.65 (age) | P=0.0001 | ||
| X | 40.76 + 1.02 (age) | P=0.0001 | P<0.0001 | |
| Y | −22.25 – 0.23 (age) | P=0.242 | ||
| X | 33.03 + 1.69 (age) | P=0.0001 | P=0.0001 | |
| Y | −53.28 – 0.63 (age) | P=0.007 | ||
| X | 31.10 + 2.07 (age) | P=0.0001 | P=0.0001 | |
| Y | −63.14 – 0.95 (age) | P=0.0004 | ||
| X | 24.7 + 2.13 (age) | P=0.0001 | P=0.0001 | |
| Y | −68.03 – 1.17 (age) | P=0.0001 | ||
| X | −19.43 – 0.05 (age) | P=0.820 | P=0.0001 | |
| Y | −42.69 – 1.82 (age) | P=0.0001 | ||
| X | −30.10 – 0.28 (age) | P=0.031 | P=0.0002 | |
| Y | −13.39 – 0.76 (age) | P=0.0004 | ||
| X | −40.41 – 0.04 (age) | P=0.812 | P=0.060 | |
| Y | −26.31 – 0.48 (age) | P=0.0020 | ||
| X | 41.02 + 1.54 (age) | P=0.0001 | P=0.0001 | |
| Y | −39.32 – 0.50 (age) | P=0.034 | ||
| X | 38.63 + 1. 47 (age) | P=0.0001 | P=0.0001 | |
| Y | −38.08 – 0.38 (age) | P=0.090 | ||
| X | 11.99 + 1.56 (age) | P=0.0001 | P=0.0001 | |
| Y | −28.54 – 1.02 (age) | P=0.0001 |
Fig 2Graphic model (Template) derived by the second method