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510(k) Data Aggregation
(77 days)
A soft reline material for use by trained dental professionals. The material is a self-polymerizing material that is used to reline the tissue contacting resin surfaces of removable prostheses, either full or partial, to enhance the comfort and fit of said removable prosthetic appliances.
Parkell's CSR is a self-curing (auto-polymerizing) silicone material that is intended for use by trained and licensed dental professionals as an easy-to-use soft reline material for removable prosthetic appliances, either full or partial, to enhance their fit and comfort. The cured silicone material is affixed to the tissue contacting surfaces of said prosthetic appliances and adheres thereto via the supplied adhesive which is simply applied with a brush. After adhesive application, the CSR silicone is expressed by means of a commonly available impression gun which causes the material to mix within an attached tip so that applied CSR is fully and completely mixed immediately upon its application to the tissue contacting surfaces of a removable prosthetic appliance. Mixed and polymerized CSR remains soft and adherent to tissue contacting over significant periods of time. The adherent silicone is akin to currently available addition-reaction silicone impression materials. The soft reline material is supplied in 50ml cartridges designed for use with impression guns, and kits also include a coating agent or glaze and adhesive necessary to cause a bond between the denture surfaces and the applied silicone.
This document is a 510(k) premarket notification for a dental product, CSR, a soft denture reline material. It outlines the product's description, intended use, and equivalence to existing devices. However, this submission does not contain acceptance criteria or a study proving device performance against such criteria.
The document focuses on demonstrating substantial equivalence to predicate devices already on the market, which is the primary requirement for a 510(k) submission. It does not include specific performance metrics, clinical study results, or an analysis of how the device meets predefined acceptance criteria in the manner typically found for a diagnostic or AI-driven medical device.
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