Correlation between Wilkes staging and arthroscopic findings in TMJ internal derangements: A 20-year retrospective study.

PURPOSE: The Wilkes classification is widely used to guide diagnosis and surgical decision-making in temporomandibular joint (TMJ) internal derangements. However, its ability to predict true intra-articular pathology remains uncertain. The purpose of this study was to evaluate the agreement between the Wilkes clinical-radiologic classification and the Bronstein-Merrill arthroscopic staging system.

MATERIALS AND

METHODS

A retrospective observational study was conducted using the TMJ arthroscopy database of the Department of Oral and Maxillofacial Surgery at Hospital Clínico San Carlos (Madrid, Spain). Patients who underwent TMJ arthroscopy between 1998 and 2018 were included. Preoperative Wilkes stages were assigned based on clinical assessment and magnetic resonance imaging. Intraoperative findings were classified according to the Bronstein-Merrill system. Agreement between classifications was assessed using weighted Cohen's kappa coefficient. Concordance patterns were analyzed with contingency tables and visualized using a Sankey flow diagram.

RESULTS

A total of 487 patients (829 joints) were analyzed. Overall agreement between Wilkes and Bronstein-Merrill classifications was poor, with exact concordance observed in 18.7% of joints and an overall discordance rate of 81.3% (κ = -0.019; p < 0.001). More than 80% of joints classified as Wilkes I exhibited pathological arthroscopic findings. Nearly 70% of Wilkes III joints and approximately one-third of Wilkes IV joints corresponded to mild arthroscopic stages (Bronstein I-II). Only 42.2% of Wilkes V joints demonstrated severe arthroscopic pathology, while 22.2% showed no significant intra-articular abnormalities.

CONCLUSIONS

The Wilkes classification demonstrates limited reliability for predicting intra-articular disease severity observed during TMJ arthroscopy. Surgical decision-making should not rely solely on Wilkes staging and should incorporate a multimodal diagnostic approach to optimize patient selection.