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Harvest Dental HD Gum Strip is indicated for adding texture, form and color to the gingival area of a dental prosthesis which includes crowns, bridges and full and partial prostheses. This composite strip is bondable to polymethylmethacrylate (PMMA), metals and ceramics (zirconia).

Harvest Dental HD Gum Strip is a bondable, light-cured composite for adding texture and color to the gingival area of a dental prosthesis which includes crowns, bridges and full and partial prostheses. This composite strip is bondable to polymethylmethacrylate (PMMA), metals and ceramics (zirconia). It is provided in a textured, malleable strip meant to be applied to a dentine/bone-like substructure. The strip is an alternative to the method of using a shaded composite paste to manually build-up and model visible gingival surfaces to look similar to natural gingival tissues. The Harvest Dental HD Gum Strip includes features such as gingival veins formed into its surface as well as being available in various shades of coloration. The strip can be easily cut, applied to the gingival surface of the restoration, then trimmed and finished with appropriate features such as gingival line and interdental papilla. Gum Strip Modifiers (paste versions of the same composite) are available in various shades and used for additional surface features and gingival repairs, and Liquid Modifiers (stains, which are a low-viscosity color-concentrated form of the same composite) are available in several shades and used for enhanced coloration resembling the natural gingiva. The Harvest Dental HD Gum Strip is bonded to the dental prosthesis utilizing a bonding adhesive provided with the strip and modifiers and, after any final detailing and coloring, is to be cured utilizing a typical light-curing device.

The provided document is a 510(k) premarket notification for a dental device, Harvest Dental HD Gum Strip. It details the device's characteristics and its substantial equivalence to a predicate device, Gradia Gum. However, this document does not contain information about acceptance criteria or a study proving the device meets those criteria in the context of an AI/ML device. The document primarily focuses on biocompatibility and mechanical property testing for a physical dental material. Therefore, most of the requested information regarding AI/ML device evaluation is not present in this document.

Here's the information that can be extracted or inferred based on the provided text, with many fields explicitly stated as "Not Applicable" for an AI/ML device, as this document is not about one.

1. A table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Not applicable as this is not an AI/ML device studying a dataset. The testing refers to physical material property tests.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Not applicable as this is not an AI/ML device. Ground truth for material properties is established through standardized physical tests.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable as this is not an AI/ML device.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
Not applicable as this is not an AI/ML device. No human-in-the-loop studies were conducted for this physical dental material.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable as this is not an AI/ML device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
For physical material properties, the "ground truth" is defined by the standards and methods used in the specified ISO tests (ISO 7405 and ISO 10477).

8. The sample size for the training set
Not applicable as this is not an AI/ML device.

9. How the ground truth for the training set was established
Not applicable as this is not an AI/ML device.

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