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Title: Reliability of Allen Ferguson classification versus subaxial injury classification and severity scale for subaxial cervical spine injuries: a psychometrics study. Author: Kanagaraju V, Yelamarthy PKK, Chhabra HS, Shetty AP, Nanda A, Sangondimath GM, Dutta Das K, Bansal ML, Mohapatra B, Patel N, Abel R, Tuli S, Barros T, Tandon V. Journal: Spinal Cord; 2019 Jan; 57(1):26-32. PubMed ID: 30089891. Abstract: STUDY DESIGN: A psychometrics study. OBJECTIVES: To determine intra and inter-observer reliability of Allen Ferguson system (AF) and sub-axial injury classification and severity scale (SLIC), two sub axial cervical spine injury (SACI) classification systems. SETTING: Online multi-national study METHODS: Clinico-radiological data of 34 random patients with traumatic SACI were distributed as power point presentations to 13 spine surgeons of the Spine Trauma Study Group of ISCoS from seven different institutions. They were advised to classify patients using AF and SLIC systems. A reference guide of the two systems had been mailed to them earlier. After 6 weeks, the same cases were re-presented to them in a different order for classification using both systems. Intra and inter-observer reliability scores were calculated and analysed with Fleiss Kappa coefficient (k value) for both the systems and Intraclass correlation coefficient(ICC) for the SLIC. RESULTS: Allen Ferguson system displayed a uniformly moderate inter and intra-observer reliability. SLIC showed slight to fair inter-observer reliability and fair to substantial intra-observer reliability. AF mechanistic types showed better inter-observer reliability than the SLIC morphological types. Within SLIC, the total SLIC had the least inter-observer agreement and the SLIC neurology had the highest intra-observer agreement. CONCLUSION: This first external reliability study shows a better reliability for AF as compared to SLIC system. Among the SLIC variables, the DLC status and the total SLIC had least agreement. Low-reliability highlights the need for improving the existing classification systems or coming out with newer ones that consider limitations of the existing ones.[Abstract] [Full Text] [Related] [New Search]