Conceptual development of a restorative dental prognosis index: A proof-of-concept framework integrating survival...

Restorative dentistry lacks an integrated prognostic framework linking restorability assessment to long-term survival evidence. This study evaluates whether published 10-year survival data can be systematically anchored to an existing multi-domain restorability index to develop a structured Restorative Dental Prognosis Index (RDPI).

A theory-driven, five-stage conceptual methodology was applied. A structured literature search identified validated restorability or risk indices as a conceptual anchor. Prognostic features were classified by relevance to restorative outcomes. Published 10-year survival estimates and uncertainty intervals were extracted from systematic reviews and re-analysed to characterise dispersion. Estimates were mapped onto an ordinal prognostic structure to minimise overfitting in heterogeneous evidence. Scenario modelling explored internal coherence, recognising differences between whole-mouth and tooth-level risk. No empirical validation was undertaken.

A single multidomain index met eligibility criteria. Across 13 domains, 10-year survival estimates showed wide variation (74-93%), very broad prediction intervals, and high heterogeneity (I²=74.8-99.8%). Despite this, survival data aligned with a three-category ordinal structure, generating a coherent prognostic scaffold. Scenario modelling demonstrated non-linear increases in whole-mouth risk and integration of biological risk with financial exposure.

By mapping long-term survival evidence onto a validated multidomain restorability framework, this proof-of-concept framework provides a structurally consistent prognostic scaffold. While not a predictive model, it offers a foundation for future empirical parameterisation, validation, and integration into restorative treatment planning, teaching, and practice.

This framework integrates multi-domain restorability assessment with long-term survival evidence to support whole-mouth prognostic reasoning. By simplifying heterogeneous data into ordinal risk states and enabling aggregation, it may help clinicians compare strategies, anticipate failure risk, and align decisions with proportionality, cost exposure, and patient-centred outcomes.