Predictive Factors and Risk Stratification Approaches in Posterior Mandibular Implant Surgery: A Systematic Review Protocol

Background
Implant-supported rehabilitation has become a predictable and widely accepted treatment modality for replacing missing teeth. However, implant placement in the posterior mandible remains challenging because of anatomical constraints and the proximity of critical neurovascular structures, particularly the inferior alveolar nerve. Recent advances in digital dentistry, computer-guided surgery, artificial intelligence (AI), and predictive analytics have created new opportunities for improving risk assessment and clinical decision-making.
Nevertheless, evidence regarding predictive factors and risk stratification approaches remains fragmented and heterogeneous.
Methods
This systematic review will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines. Electronic searches will be performed in PubMed/MEDLINE, EMBASE, Cochrane Library, Web of Science, Scopus, ScienceDirect, IEEE Xplore, LILACS, and African Index Medicus. Additional searches will be conducted in ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform (ICTRP), and reference lists of eligible studies. Studies evaluating predictive factors, prognostic determinants, prediction models, artificial intelligence applications, and decision-support systems related to posterior mandibular implant surgery will be considered. Risk of bias will be assessed using the QUIPS and PROBAST tools. A narrative synthesis will be conducted.
Results
This review will provide a comprehensive synthesis of predictive factors associated with implant success, implant failure, and surgical complications in posterior mandibular implant surgery. Existing predictive models and decision-support approaches will be critically evaluated with regard to methodological quality, applicability, and clinical relevance.
Conclusion
This systematic review aims to establish an evidence-based framework for multidimensional risk stratification in posterior mandibular implant surgery and to identify future directions for the development of context-sensitive predictive models applicable across diverse healthcare settings.