Predictors of dental arch abnormalities in children with primary dentition (part two)

Relevance. Assessing the influence of risk factors during the primary dentition period on the development of dental arch abnormalities is essential for planning preventive and therapeutic interventions, as well as encouraging compliance with these measures. The first part of this article focused on identifying predictors of malocclusion, while this second part aims to determine the predictors of dental arch abnormalities. Purpose. To identify prognostic factors (predictors) of dental arch abnormalities in children during the primary dentition period. Materials and methods. This study presents the results of a retrospective analysis of the oral health status of 123 children (55 boys and 68 girls). The initial examination took place when the children were between 4.0 and 5.5 years old (mean age 5.1 ± 0.6 years), with follow-up examinations conducted between the ages of 6.0 and 10.5 years (mean age 8.7 ± 1.3 years). The relationship between risk factors in primary dentition and the development of dental arch abnormalities in early mixed dentition was evaluated using Pearson's chi-squared test (χ 2 ) and Welch's t-test (V). For each pair of "primary dentition risk factor – dental arch abnormality in early mixed dentition," the odds ratio with a 95% confidence interval was calculated. Results . Differentiating predisposing factors and analyzing the effects of their various combinations allowed the identification of predictor clusters in primary dentition, where the probability of developing dental arch abnormalities in mixed dentition exceeds 95%. For upper arch abnormalities, the cluster includes infantile swallowing and early extraction of deciduous molars in the lower jaw (χ 2 = 19.67, V = 0.50); for lower arch abnormalities, the cluster consists of infantile swallowing, "lazy" chewing, sucking habits, and a deep bite in the anterior region (χ 2 = 16.58, V = 0.67); For lower incisor crowding, the cluster includes "lazy" chewing, infantile swallowing, and a deep bite in the anterior region (χ 2 = 17.54, V = 0.63); for diastema in the upper arch, the cluster includes an abnormal labial frenum and interdental tongue position (χ 2 = 19.16, V = 0.49); for maxillary anterior spacing, early extraction of deciduous canines in the upper jaw is the primary factor (χ 2 = 16.23, V = 0.46). Conclusion . Identifying and addressing predictors of dental arch abnormalities in children during the primary dentition period can significantly reduce the risk of developing pathological conditions during the mixed dentition period.

Authors

Institutions

Publication Details

Journal
Pediatric dentistry and dental prophylaxis
Published
2024-10-25
DOI
https://doi.org/10.33925/1683-3031-2024-767
Citations
1
Primary Topic
Orthodontics and Dentofacial Orthopedics
Type
article
Field-Weighted Citation Impact
0.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictors of dental arch abnormalities in children with primary dentition (part two)

М. А. Данилова, П. В. Ишмурзин, T. I. Rudavina
1 citations
Pediatric dentistry and dental prophylaxis
Orthodontics and Dentofacial Orthopedics
0.40
article

Predictors of dental arch abnormalities in children with primary dentition (part two)

М. А. Данилова, П. В. Ишмурзин, T. I. Rudavina
article en
1 citations

Abstract

Relevance. Assessing the influence of risk factors during the primary dentition period on the development of dental arch abnormalities is essential for planning preventive and therapeutic interventions, as well as encouraging compliance with these measures. The first part of this article focused on identifying predictors of malocclusion, while this second part aims to determine the predictors of dental arch abnormalities. Purpose. To identify prognostic factors (predictors) of dental arch abnormalities in children during the primary dentition period. Materials and methods. This study presents the results of a retrospective analysis of the oral health status of 123 children (55 boys and 68 girls). The initial examination took place when the children were between 4.0 and 5.5 years old (mean age 5.1 ± 0.6 years), with follow-up examinations conducted between the ages of 6.0 and 10.5 years (mean age 8.7 ± 1.3 years). The relationship between risk factors in primary dentition and the development of dental arch abnormalities in early mixed dentition was evaluated using Pearson's chi-squared test (χ 2 ) and Welch's t-test (V). For each pair of "primary dentition risk factor – dental arch abnormality in early mixed dentition," the odds ratio with a 95% confidence interval was calculated. Results . Differentiating predisposing factors and analyzing the effects of their various combinations allowed the identification of predictor clusters in primary dentition, where the probability of developing dental arch abnormalities in mixed dentition exceeds 95%. For upper arch abnormalities, the cluster includes infantile swallowing and early extraction of deciduous molars in the lower jaw (χ 2 = 19.67, V = 0.50); for lower arch abnormalities, the cluster consists of infantile swallowing, "lazy" chewing, sucking habits, and a deep bite in the anterior region (χ 2 = 16.58, V = 0.67); For lower incisor crowding, the cluster includes "lazy" chewing, infantile swallowing, and a deep bite in the anterior region (χ 2 = 17.54, V = 0.63); for diastema in the upper arch, the cluster includes an abnormal labial frenum and interdental tongue position (χ 2 = 19.16, V = 0.49); for maxillary anterior spacing, early extraction of deciduous canines in the upper jaw is the primary factor (χ 2 = 16.23, V = 0.46). Conclusion . Identifying and addressing predictors of dental arch abnormalities in children during the primary dentition period can significantly reduce the risk of developing pathological conditions during the mixed dentition period.

Pediatric dentistry and dental prophylaxisVol. 24(3)
Perm State Medical Academy (RU)
Openalex Percentile: Top 41%
Orthodontics and Dentofacial Orthopedics
0.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.