Percorrer por autor "Angelo, David Faustino"
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- Improving diagnostic models for temporomandibular disease using cost-effective variables:an analysis of the Dimitroulis ClassificationPublication . Geraldes, Carlos Brás; São João, Ricardo; Cardoso, Henrique José; Angelo, David FaustinoTemporomandibular disorders (TMD) are a class of degenerative musculoskeletal and neuromuscular conditions involving the temporomandibular joint (TMJ) complex and surrounding musculature. The etiology of TMD is multifactorial, including biological,environmental, social, emotional, and cognitive triggers. Due to the complexity of the disease’s signs and symptoms, the diagnosis and correct treatment of TMD remain a challenge. The Dimitroulis classification (DC) divides TMD into five categories (DC1, DC2, . . . , DC5) based on the degree of disease severity with an indication for treatment. The classification is based on history and physical examination and diagnostic imaging is used to access intra-articular derangements. This process presented some subjectivity in the analysis and, has significant associated costs. The present study aims to identify variables based on patient complaints with lower associated costs and more objective, prompt, and less burdensome classification.
- A predictive model for arthrogenous temporomandibular disorders based on clinical signs and symptomsPublication . Angelo, David Faustino; Cardoso, Henrique José; Geraldes, Carlos; São João, Ricardo; Maffia, Francesco; Sanz, David; Salvado, FranciscoThis study aimed to develop and internally evaluate a multivariable statistical model to identify arthrogenous temporomandibular disorders (TMD) using routinely collected clinical data. The model's performance was compared with the Fonseca Anamnestic Index (FAI) alone, using an imaging-based classification as the reference standard. This cross-sectional observational study included 1170 consecutive patients attending their first consultation at a tertiary TMD center between August 2019 and August 2024. Arthrogenous TMD was deter mined using combined clinical and imaging assessment according to the Dimitroulis classification. Clinical variables, including age, maximum mouth opening (MMO), individual FAI items, and joint-related complaints, were extracted from the EUROTMJ database. Generalized additive models (GAMs) were used to develop pre dictive models. Performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, and specificity in training (60%) and test (40%) datasets. The final Fonseca–Dimitroulis (FD-Class) model incorporated age, MMO, selected FAI items (Q2, Q6, Q7), crepitus, and temporomandibular joint (TMJ) locking. The model achieved an AUC of 0.761 in the training dataset and 0.742 in the test dataset, outperforming the FAI alone (AUC = 0.662). This model may support the early identification of arthrogenous TMJ disease and improve decision-making regarding referral for advanced imaging in maxillofacial practice.This study aimed to develop and internally evaluate a multivariable statistical model to identify arthrogenous temporomandibular disorders (TMD) using routinely collected clinical data. The model's performance was compared with the Fonseca Anamnestic Index (FAI) alone, using an imaging-based classification as the reference standard. This cross-sectional observational study included 1170 consecutive patients attending their first consultation at a tertiary TMD center between August 2019 and August 2024. Arthrogenous TMD was deter mined using combined clinical and imaging assessment according to the Dimitroulis classification. Clinical variables, including age, maximum mouth opening (MMO), individual FAI items, and joint-related complaints, were extracted from the EUROTMJ database. Generalized additive models (GAMs) were used to develop pre dictive models. Performance was assessed using receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, and specificity in training (60%) and test (40%) datasets. The final Fonseca–Dimitroulis (FD-Class) model incorporated age, MMO, selected FAI items (Q2, Q6, Q7), crepitus, and temporomandibular joint (TMJ) locking. The model achieved an AUC of 0.761 in the training dataset and 0.742 in the test dataset, outperforming the FAI alone (AUC = 0.662). This model may support the early identification of arthrogenous TMJ disease and improve decision-making regarding referral for advanced imaging in maxillofacial practice.