کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
3168761 1199427 2008 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Likelihood ratio methodology to identify predictors of treatment outcome in temporomandibular joint arthralgia patients
موضوعات مرتبط
علوم پزشکی و سلامت پزشکی و دندانپزشکی دندانپزشکی، جراحی دهان و پزشکی
پیش نمایش صفحه اول مقاله
Likelihood ratio methodology to identify predictors of treatment outcome in temporomandibular joint arthralgia patients
چکیده انگلیسی

ObjectivesThe purpose of this prospective, cohort study of patients with temporomandibular joint (TMJ) pain was to develop rules to predict treatment outcome related to occlusal stabilization splints.Study designThe study comprised 119 patients with a unilateral Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) axis I diagnosis of arthralgia. Visual analog scale (VAS) pain level of function was assessed before stabilization splint therapy and compared with the respective 2-month and 6-month follow-up findings. Magnetic resonance (MR) images were obtained immediately before treatment to establish the presence or absence of disk displacement, osteoarthrosis, effusion, and bone marrow edema. Treatment outcome (success or failure) was categorized based on changes in the VAS pain level after 6 months.ResultsSixty-five (55%) subjects were categorized as treatment success, 17 (14%) as treatment failures, and 37 (31%) as somewhat improved. After using univariate analyis to determine the association between potential clinical and MR imaging predictor variables and treatment outcome status, preliminary prediction rules were developed for prediction of success (positive LR, 10.8; 95% confidence interval [CI], 0.6-188.1) and failure (negative LR, 0.05; CI, 0.0-0.8). The most important variables were time since pain onset, basic VAS pain level, change in VAS level at 2-month follow-up, and clinical diagnoses of disk displacement with and without reduction.ConclusionOutcome following use of occlusal stabilization splints may be predicted from variables collected from self-report and physical examination. Predictive modeling may provide clinicians with the opportunity to identify “at-risk” patients early and initiate alternative treatment approaches.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology - Volume 106, Issue 4, October 2008, Pages 525–533
نویسندگان
, ,