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J Korean Soc Laryngol Phoniatr Logop > Volume 15(2); 2004 > Article
Journal of The Korean Society of Laryngology, Phoniatrics and Logopedics 2004;15(2): 112-117.
성대 결절 및 폴립 병변 판별 예측모형에 대한 연구
박수정1, 심현섭1, 정성민2, 김한수2, 박애경3
1이화여자대학교 대학원 언어병리협동과정
2이화여자대학교 의과대학 이비인후과학교실
3이화여자대학교 의과대학 음성언어치료실
A Study of the Lesional Grade Discrimination Model for Vocal Fold Nodules and Polyps
Soo-Jung Park1, Hyun-Sup Shim1, Sung-Min Chung2, Han-Soo Kim2, Ae-Kyung Park3
1Interdisciplinary Program of Communication Disorders, The Graduate School
2Department of Otolaryngology, College of Medicine, Ewha Womans University
3Phoniatric Laboratory, College of Medicine, Ewha Womans University
ABSTRACT
Background and Objectives
: This study is purposed to investigate the statistically significant discrimination model for predicting vocal fold nodule and polyp's lesional grade, with patients' background data and objective voice evaluation parameters.
Materials and Method
: The retrospective research was carried out at the Ewha Womans University Hospital. 122 patients' voice examination data had been selected, and lesion screening (Grade I, II, and III) was conducted by 2 ENT specialists, with each patient's vocal fold pictures achieved during the laryngoscopy examination.
Results
: The Lesional Grade Discrimination Model with which the lesional grade of vocal fold nodules and polyps could be predicted was derived by the ordinal logistic regression analysis (using SPSS 10.0). With this model the lesional grades of 73 out of 122 patients(59.8%) were correctly predicted to their formerly screened ones.
Conclusion
: This model applied the multivariate approach, which statistically combined these currently used parameters, Jitter, Shimmer, MFR, MPT, and patient's background data such as gender and dysphonia period. It might explain the status of benign lesion of vocal folds, and furthermore expect the physiological function of vocal folds.
KEY WORDS: Vocal folds nodules;Polyps;Lesional grade discrimination model;Objective voice evaluation;Ordinal logistic regression analysis;
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