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Kettosil® and Silginat® (Silginat®, Silginat® Strawberry) Impression Materials are addition-curing, elastomeric preliminary impression materials, to be used as an alternative to traditional alginate materials.

Kettosil, Silginat and Silginat Strawberry are suitable for impressions in preparation of:

• Preliminary impression
• Anatomical models
• Fabricating temporary crowns and bridges
• Opposing dentition
• Fabricating simple removable prosthetic restorations
• Orthodontic work
• Fabricating removable retainers and splints
• Case study models
• Producing models for the construction of trays for whitening solutions, bites and surgical guides.

Kettosil® and Silginat® (Silginat®, Silginat® Strawberry) Impression Materials are addition-curing, elastomeric preliminary impression materials, to be used as an alternative to traditional alginate materials. They differ in their curing times, final hardnesses, and recovery from deformation.

The provided text is for a 510(k) premarket notification for dental impression materials. This type of submission focuses on demonstrating substantial equivalence to a predicate device based on technological characteristics and performance testing to established standards. It does not typically involve the kinds of human reader or AI performance studies you've requested.

Therefore, many of your requested items, such as multi-reader multi-case studies, standalone algorithm performance, number of experts for ground truth, and training set details, are not applicable to this specific regulatory document.

However, I can extract information related to acceptance criteria and the general study approach for this type of device:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample Size: Not explicitly stated in the provided text for a "test set" in the context of clinical or AI studies. The performance data refers to evaluation against standards, which typically involves laboratory testing of material samples, not a patient-based test set.
• Data Provenance: Not applicable in the sense of country of origin or retrospective/prospective for this type of materials testing. Performance testing would be laboratory-based.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not Applicable: This type of information is generally relevant for AI/clinical studies involving human interpretation or diagnosis. For dental impression materials, "ground truth" is established through physical and chemical property measurements against international standards.

4. Adjudication method for the test set

• Not Applicable: Adjudication is typically used for resolving discrepancies in expert interpretations in clinical studies. For material performance, the "adjudication" is whether the material's properties meet the specified values in the standards.

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

• No: No multi-reader multi-case comparative effectiveness study was conducted. This device is a physical material, not an AI or imaging diagnostic tool that would typically involve human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• No: This is a physical dental impression material, not a standalone algorithm.

7. The type of ground truth used

• Standard-based Physical and Chemical Properties: The ground truth for this device's performance is derived from established international standards (ISO 4823) and FDA guidance for dental impression materials, which define acceptable ranges for physical and chemical properties (e.g., curing times, final hardnesses, recovery from deformation).

8. The sample size for the training set

• Not Applicable: This device does not involve machine learning algorithms or a "training set" in that context.

9. How the ground truth for the training set was established

• Not Applicable: No training set as per a machine learning or AI context. The performance of the material itself is evaluated against predefined material science standards.

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