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Methods : Short-term follow-up of masticatory adaptation after rehabilitation with an immediately loaded implant-supported prosthesis: a pilot assessment [2]

Methods : Short-term follow-up of masticatory adaptation after rehabilitation with an immediately loaded implant-supported prosthesis: a pilot assessment [2]

author: Mihoko Tanaka, Collaert Bruno, Reinhilde Jacobs, Tetsurou Torisu, Hiroshi Murata | publisher: drg. Andreas Tjandra, Sp. Perio, FISID

To assess the masticatory efficiency, we used glucose extraction in the filtrate obtained after chewing the specimen. After rinsing the mouth with tap water, a gum-like specimen mixed with 5% glucose with a height of 10 mm (Glucosensor Gummy, GC, Tokyo, Japan) was placed on patient’s tongue with chopsticks. Patients were requested to chew on the cube for 20 s, after which, they expectorated all the chunks of the cube into a cup equipped with a mesh filter to hold the debris. Thereafter, they rinsed their mouth again with 10 ml of water and expectorated into the same cup. The amount of glucose extraction in the filtrate obtained after chewing the specimen was used as a measure of masticatory efficiency. Glucose concentration in the filtrate (mg/dl) was measured using a calibrated Glucose Sensor Set (Glucosensor GS-1, GC, Tokyo, Japan), which utilizes a glucose sensor for diabetics (Accu-check Comfort, Roche Diagnostic, Basel, Switzerland) to measure masticatory efficiency according to a previous study, which reported its reliability for the evaluation of masticatory function [40]. For reproducibility, we tested the glucose concentration of control glucose solutions (500, 250, 125, 100, and 50 mg/dl) with the glucose sensor. The linear relationship that was observed between the glucose density of the solution (x) and the masticatory efficiency (the value of the glucose sensor) (y) is displayed in a scatter diagram (Fig. 1). The linear regression equation and Pearson’s correlation coefficient were as follows: y = 0.599 + 1.066x, r = 0.99 (n = 50, p< 0.0001). The intra-class correlation coefficient (ICC) is a prominent statistic to measure the test-retest reliability of data. The ICC (1, 3) of the data by Glucosensor was p = 1.000 (n = 5).

Three types of chewing specimen with different levels of hardness (hard, medium, and soft), with the same size and taste, were produced from sucrose (800 g), glucose (870 g), sorbitol (1000 g), gelatin (hard, 390 g; medium, 240 g; and soft, 150 g), Arabia gum (hard, 36 g), citric acid (42 g), lemon juice (15 g), and water and were 15 × 15 ×10 mm in size. The hardness of each type was determined under maximal stress during compression of 9 mm with a crosshead speed of 100 mm/min with a tooth-shaped jig using a texture analyzer (EZ test, Shimadzu Co., Kyoto, Japan). The hardness results were 73 ± 1.5 N for the soft, 88 ± 1.5 N for the medium, and 171 ± 1.9 N for the hard specimens.

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