Minerva dental and oral science · 2026
Comparing polynomial regression and exponential growth model in predicting recession gain based on clinical gingival recession.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kumar Yadalam Pradeep, Marrapodi Maria Maddalena, Ronsivalle Vincenzo, Franco Rocco, Bollero Patrizio, Cicciù Marco, Minervini Giuseppe
Аннотация
BACKGROUND: Gingival recession (GR) is a common issue causing plaque accumulation, root caries, abrasion, cervical wear, and dentin hypersensitivity. Studies show a prevalence of thick and thin gingival biotypes, with no significant association between age, gender, or recession. This study aims to use polynomial regression and exponential growth models to predict recession gain based on clinical data. GR is a common issue causing plaque accumulation, root caries, abrasion, cervical wear, and dentin hypersensitivity. Studies show a prevalence of thick and thin gingival biotypes, with no significant association between age, gender, or recession. This study aims to use polynomial regression and exponential growth models to predict recession gain based on clinical data.
METHODS: The study analyzed recession coverage in upper and lower anterior teeth after connective tissue and free gingival grafting procedures from institute database. Data was preprocessed and analyzed using polynomial regression and exponential growth models. Polynomial regression allowed for nonlinear trends in recession gain, while exponential growth models provided insights into cumulative effects of specific factors on recession progression.
RESULTS: The polynomial of degree 3 with the lowest MSE of 0.0612 fit best. This shows that cubic polynomials better represent the "Days"-"Gain Width" relationship than linear or higher-degree polynomials. The exponential growth model fitted to "Post Op" data predicts an initial value of 0.7735 and a growth rate of 0.0101. This model proposes that "Post Op" measurements expand exponentially, with predicted parameters reflecting this growth.
CONCLUSIONS: Based on clinical data, this study compares polynomial regression and exponential growth models for periodontal recession gain prediction. The findings emphasize the need of proper mathematical models to better understand periodontal diseases and guide dental care prevention and treatment.
METHODS: The study analyzed recession coverage in upper and lower anterior teeth after connective tissue and free gingival grafting procedures from institute database. Data was preprocessed and analyzed using polynomial regression and exponential growth models. Polynomial regression allowed for nonlinear trends in recession gain, while exponential growth models provided insights into cumulative effects of specific factors on recession progression.
RESULTS: The polynomial of degree 3 with the lowest MSE of 0.0612 fit best. This shows that cubic polynomials better represent the "Days"-"Gain Width" relationship than linear or higher-degree polynomials. The exponential growth model fitted to "Post Op" data predicts an initial value of 0.7735 and a growth rate of 0.0101. This model proposes that "Post Op" measurements expand exponentially, with predicted parameters reflecting this growth.
CONCLUSIONS: Based on clinical data, this study compares polynomial regression and exponential growth models for periodontal recession gain prediction. The findings emphasize the need of proper mathematical models to better understand periodontal diseases and guide dental care prevention and treatment.