Journal of clinical periodontology · 2026
Methodological Quality of Prognostic Models for Periodontitis: A Systematic Review and Critical Appraisal.
СтатьяСистематический обзорНазвание 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.
Karaban Maryia, Dias Debora R, Troiano Giuseppe, Baniameri Sahar, Ahuja Ishan, Joseph Divya, Serroni Matteo, Ravidà Andrea
Аннотация
AIM: To critically appraise the methodological quality and historical evolution of prognostic models for periodontitis.
MATERIALS AND METHODS: A systematic search was conducted in three databases. Eligible studies were classified as conceptual tools, category-based tools and data-driven models. Category-based and data-driven models were evaluated with PROBAST. Risk of bias (ROB) was compared across four domains: Participants, Predictors, Outcome and Analysis.
RESULTS: Twenty-seven studies were included. Prognostic methods evolved from qualitative risk categories and expert-based classifications towards quantitative, individualised probability estimates generated through multivariable statistical and machine-learning models, alongside progressive improvements in outcome definition, prediction time and risk expression formats. Conceptual tools were not evaluable under PROBAST due to the absence of empirical data. Category-based tools showed limitations in predictor handling and validation rather than bias per se. Data-driven models demonstrated progressive refinement: earlier studies lacked consistent validation, whereas recent models more frequently report internal validation, calibration metrics and external validation. However, all studies except one were classified as high ROB. Domain-level assessment showed strengths in the Participants domain but persistent limitations in the Analysis domain. Methodological improvements after TRIPOD were modest.
CONCLUSION: Prognostic models reflect distinct methodological profiles across design type. Conceptual and category-based frameworks remain clinically relevant historically, while data-driven models represent the contemporary standard but retain important methodological gaps.
TRIAL REGISTRATION: PROSPERO number: CRD42024525505.
MATERIALS AND METHODS: A systematic search was conducted in three databases. Eligible studies were classified as conceptual tools, category-based tools and data-driven models. Category-based and data-driven models were evaluated with PROBAST. Risk of bias (ROB) was compared across four domains: Participants, Predictors, Outcome and Analysis.
RESULTS: Twenty-seven studies were included. Prognostic methods evolved from qualitative risk categories and expert-based classifications towards quantitative, individualised probability estimates generated through multivariable statistical and machine-learning models, alongside progressive improvements in outcome definition, prediction time and risk expression formats. Conceptual tools were not evaluable under PROBAST due to the absence of empirical data. Category-based tools showed limitations in predictor handling and validation rather than bias per se. Data-driven models demonstrated progressive refinement: earlier studies lacked consistent validation, whereas recent models more frequently report internal validation, calibration metrics and external validation. However, all studies except one were classified as high ROB. Domain-level assessment showed strengths in the Participants domain but persistent limitations in the Analysis domain. Methodological improvements after TRIPOD were modest.
CONCLUSION: Prognostic models reflect distinct methodological profiles across design type. Conceptual and category-based frameworks remain clinically relevant historically, while data-driven models represent the contemporary standard but retain important methodological gaps.
TRIAL REGISTRATION: PROSPERO number: CRD42024525505.