Risk prediction in dental implants with bone substitutes using a hybrid neutrosophic-logistic model: Managing uncertainty in clinical and biomechanical variables

Predicting risk for bone substitutes when using dental implants is a complicated task as no one can determine all clinical and biomechanical factors that will/may contribute to a successful treatment outcome. Yet the discipline has been establishing various means of prediction through traditional logistic regression. However, many do not acknowledge the unknowns--potential developments of one's biological response or biomaterial characteristics. This work seeks to adjust this issue through a novel approach to traditional logistic regression and neutrosophic logic, which, combined, can calculate levels of certainty, indeterminacy and uncertainty from one dataset. In this example, for example, the variables used for regression are assigned truth, indeterminacy and falsity renders the model a more accurate representation of the confounding complicated factors that arise in real clinical practice. The resulting model demonstrates results that would never be seen without multidimensional analysis as this encourages findings beyond just true/false (ie the average age of patients or identical studies which provide bone substitute success in certain cases but failure in many others). Thus, while the traditional logistic regression can predict success or failure based on pure facts and findings, this neutrosophic intersection model improves risk assessment when information is lacking or subjectively fluctuating. Therefore, for application in clinical practice, this predictive model is best for personalized decision making as it not only presents risk factors, but also how certain one can be about them. Thus, this study contributes to a conceptual framework that transforms stringent application of statistical relevance into real-world relevance. The most significant implication for the future of today's implant dentistry is that the concept of uncertainty is universal across any medicolegal arena; therefore, courses of research should reflect such an adjustment. While this study's implementation is specific to bone substitute surgeries, the investigation's developed methodology can be implemented in any risk analysis endeavor within the realm of regenerative medicine as this reveals the groundwork was laid here to import neutrosophic findings into similar investigative endeavors. Implications pragmatically apply a better neutoapplied logical assessment of iterated uncertainty in biomedical studies. Theoretical concepts humanistically apply uncertainty and life-altering risk into a dependent aspect of risk assessment for future customized medicine alterations.

Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2025-10-01
DOI
https://doi.org/10.5281/zenodo.16425396
Primary Topic
Statistical Methods in Epidemiology
Type
article
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article

Risk prediction in dental implants with bone substitutes using a hybrid neutrosophic-logistic model: Managing uncertainty in clinical and biomechanical variables

SHILAP Revista de lepidopterología
Statistical Methods in Epidemiology
article

Risk prediction in dental implants with bone substitutes using a hybrid neutrosophic-logistic model: Managing uncertainty in clinical and biomechanical variables

article en

Abstract

Predicting risk for bone substitutes when using dental implants is a complicated task as no one can determine all clinical and biomechanical factors that will/may contribute to a successful treatment outcome. Yet the discipline has been establishing various means of prediction through traditional logistic regression. However, many do not acknowledge the unknowns--potential developments of one's biological response or biomaterial characteristics. This work seeks to adjust this issue through a novel approach to traditional logistic regression and neutrosophic logic, which, combined, can calculate levels of certainty, indeterminacy and uncertainty from one dataset. In this example, for example, the variables used for regression are assigned truth, indeterminacy and falsity renders the model a more accurate representation of the confounding complicated factors that arise in real clinical practice. The resulting model demonstrates results that would never be seen without multidimensional analysis as this encourages findings beyond just true/false (ie the average age of patients or identical studies which provide bone substitute success in certain cases but failure in many others). Thus, while the traditional logistic regression can predict success or failure based on pure facts and findings, this neutrosophic intersection model improves risk assessment when information is lacking or subjectively fluctuating. Therefore, for application in clinical practice, this predictive model is best for personalized decision making as it not only presents risk factors, but also how certain one can be about them. Thus, this study contributes to a conceptual framework that transforms stringent application of statistical relevance into real-world relevance. The most significant implication for the future of today's implant dentistry is that the concept of uncertainty is universal across any medicolegal arena; therefore, courses of research should reflect such an adjustment. While this study's implementation is specific to bone substitute surgeries, the investigation's developed methodology can be implemented in any risk analysis endeavor within the realm of regenerative medicine as this reveals the groundwork was laid here to import neutrosophic findings into similar investigative endeavors. Implications pragmatically apply a better neutoapplied logical assessment of iterated uncertainty in biomedical studies. Theoretical concepts humanistically apply uncertainty and life-altering risk into a dependent aspect of risk assessment for future customized medicine alterations.

SHILAP Revista de lepidopterología
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Statistical Methods in Epidemiology
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