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Agreement analysis of three mandibular third molar retention classifications
Robert Stick Rivera-Herrera1, Vicente Esparza-Villalpando2, Josué Roberto Bermeo-Escalona3
1Universidad Autónoma de San Luis Potosí, Facultad de Estomatología, San Luis Potosí Guanajuato. México.
Summary
The Winter classification demonstrated the best agreement for evaluating impacted mandibular third molars. Training residents found the Winter classification to have moderate agreement, unlike Pell & Gregory and Sánchez-Torres classifications.
Area of Science:
- Oral and Maxillofacial Surgery
- Radiodontology
- Dental Anatomy
Background:
- Pell & Gregory and Winter classifications are standard for third molar categorization.
- The Sánchez-Torres classification is utilized in Mexico but lacks prior evaluation.
- Accurate classification of impacted mandibular third molars is crucial for treatment planning.
Purpose of the Study:
- To evaluate the inter-observer agreement of three classifications for impacted mandibular third molars: Pell & Gregory, Winter, and Sánchez-Torres.
- To compare the reliability of these classifications in radiographic evaluations.
Main Methods:
- An observational, descriptive study assessed inter-observer agreement.
- Ten oral and maxillofacial surgeons and ten training residents evaluated impacted mandibular third molars.
- Classifications used were Pell & Gregory, Sánchez-Torres, and Winter.
- Fleiss' kappa test was employed to measure inter-observer agreement.
Main Results:
- The Pell & Gregory classification showed the lowest agreement (kappa = 0.05 and 0.185).
- The Sánchez-Torres classification yielded moderate agreement (kappa = 0.125 and 0.326).
- The Winter classification exhibited the highest agreement (kappa = 0.28 for surgeons, 0.636 for residents).
Conclusions:
- The Winter classification demonstrated the best inter-observer agreement among the evaluated methods.
- Winter classification showed acceptable, moderate agreement for training residents in classifying mandibular third molars.
- Further research may explore refining existing classifications or developing new ones for improved consistency.

