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It is intended for the permanent cementation of ceramic (including zirconia), composite and metal-based posts, crowns, bridges, veneers, inlays and onlays.

TotalC-Ram is permanent, radio-opaque dual cured (auto/photopolymerizable) self-etch and self-adhesive resin cement.

The provided document is a 510(k) premarket notification for a dental cement called "TotalC-RAM." It outlines the device's characteristics and compares it to predicate devices to demonstrate substantial equivalence to legally marketed devices.

This document does not describe a study involving AI/ML medical devices based on imaging data or other complex data types that would require acceptance criteria related to accuracy, sensitivity, specificity, or human reader performance. Therefore, most of the requested information (points 1-9) about acceptance criteria, test sets, expert ground truth, MRMC studies, standalone performance, and training sets is not applicable to this document.

Here's why and what information can be extracted:

• This is a review for a dental cement (TotalC-RAM), which is a physical material used in dental procedures.
• The comparison is based on physical and mechanical properties (e.g., shear bond strength, flexural strength, solubility, working time, setting time, film thickness, shelf life) and chemical composition.
• Section 11, "Brief Description of Performance and Biocompatibility Testing," explicitly states that the submission is an Abbreviated 510(k) and relies on conformity with guidance documents and recognized consensus standards (like ISO standards for dental materials).
• Section 12, "Clinical Performance Data," explicitly states: "Not applicable. Clinical performance testing has not been performed for the subject device." This confirms that there was no human reader study, no ground truth established by experts interpreting imaging, and no AI performance metrics.

Therefore, it is impossible to populate the requested table and answer the specific questions (1-9) as they pertain to the evaluation of an AI-based system's performance. The information requested is relevant for software-based medical devices, especially those that analyze medical images or other clinical data to provide diagnostic or prognostic insights.

To directly address your request, here's what can be inferred or stated based on the document's content, acknowledging the mismatch in context:

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

The "acceptance criteria" here are not in terms of AI performance metrics (like sensitivity/specificity) but rather the physical and mechanical properties being comparable to predicate devices or meeting ISO standards. The document doesn't explicitly state quantitative acceptance criteria for each property for the purpose of a performance study, but rather lists the measured performance of the device and compares it to predicates. The "acceptance" is that these values are within a range considered safe and effective, and contribute to substantial equivalence.

Note: The "acceptance criteria" for a physical dental cement are primarily whether its properties fall within established ranges defined by international standards (e.g., ISO 4049, ISO 9917-1) and whether it is "substantially equivalent" to predicate devices, meaning it performs similarly and does not raise new questions of safety or effectiveness. The increased shear bond strength for zirconia is highlighted as a performance advantage, but not as a strict "acceptance criterion" that it must surpass predicates.

2. Sample size used for the test set and the data provenance: Not Applicable. This is a material properties comparison, not a data-driven AI study. Laboratory tests are performed on material specimens.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not Applicable. Ground truth for material properties is established through standardized laboratory testing, not expert consensus on interpretations.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not Applicable.

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. No human readers or AI assistance involved.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not Applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Ground truth for material properties is established through standardized laboratory testing methods as outlined by ISO standards (e.g., measuring flexural strength, solubility, bond strength under controlled laboratory conditions).

8. The sample size for the training set: Not Applicable. This product is not an AI/ML device.

9. How the ground truth for the training set was established: Not Applicable. This product is not an AI/ML device.

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