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Direct restoration of Class I, II, III and V cavities (ideally Class I and II), base or liner, core build-ups.

DenMat Bulk Fill is a tooth shaded, radiopaque, dual-cured (auto-cure with light-cure acceleration) composite that offers an unlimited depth of cure in a single application without multiple layers and lightcuring when allowed to self-cure. DenMat Bulk Fill has the handling properties of a low viscosity or "flowable" composite that allows for adaptation to the prepared cavity walls. DenMat Bulk Fill is also ideal for core buildups, and any application where light transmission is inadequate. It is provided in multiple shades.

This a medical device for tooth restoration, not an AI/ML device. Thus, the questions surrounding AI/ML device performance are not applicable.

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):

The document does not specify exact sample sizes for each performance test. It refers to "Non-clinical performance evaluations" and "DenMat internal testing methods." There is no information regarding the country of origin of the data or whether it was retrospective or prospective.

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):

This is a non-AI/ML medical device submission. The concept of "ground truth" established by experts, as typically applied in AI/ML studies (e.g., radiologists interpreting images), is not relevant here. Performance criteria are based on established international standards for dental materials (e.g., ISO 4049).

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

Not applicable, as this is a non-AI/ML medical device submission. Adjudication methods are typically used in clinical trials involving human readers or evaluators, which is not the primary mode of evaluation for this materials-based 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 a non-AI/ML medical device submission.

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

Not applicable, as this is a non-AI/ML medical device submission.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

The "ground truth" for this device's performance is based on well-established international standards for dental materials (e.g., ISO 4049:2019, ANSI/ADA Standard No. 27-2016, ISO 7491:2000) and internal testing methods. For biocompatibility, it's based on ISO 10993 series of standards. These standards define the acceptable physical, chemical, and biological properties for these types of materials.

8. The sample size for the training set:

Not applicable, as this is a non-AI/ML medical device submission. There is no concept of a "training set" for this type of device.

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

Not applicable, as this is a non-AI/ML medical device submission.

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TurboTemp EZ is a is a self-cure crown-and-bridge composite indicated for the fabrication of long- and short-term provisional restorations including veneers, inlays/onlays, crowns, bridges and long-span bridges. TurboTemp EZ is also indicated for incorporation of most mechanically anchored attachment components into the acrylic base of a denture, an overdenture, or a partial denture.

TurboTemp EZ is a provisional resin based material intended to be used for the fabrication of crowns, bridges, inlays, onlays, and veneers. The composite material is provided in a dual-barrel cartridge that when combined in the mixing tip, it is dispensed as a self-curing restorative material.

The provided text describes the regulatory clearance of a dental device, "TurboTemp EZ," and includes information relevant to its performance testing and comparison to predicate devices, rather than a study proving the device meets acceptance criteria in terms of algorithm performance for an AI/ML device.

Based on the content provided, here's an analysis structured according to your request, with the understanding that this is for a physical medical device, not an AI/ML algorithm:

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

For a physical dental material, acceptance criteria often relate to physical, chemical, and biological properties, usually benchmarked against industry standards or predicate devices.

Ask a specific question about this device