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LiquiBand® Dental n-butyl cyanoacrylate is intended for use as an adhesive when bonding of caps and crowns to teeth is desired.

The LiquiBand® Dental adhesive formula consists of n-butyl cyanoacrylate monomer containing small amounts of hydroquinone (a free radical inhibitor) and sulfur dioxide (an anionic inhibitor).

LiquiBand® Dental, n-butyl cyanoacrylate adhesive, is a clear, colorless, free-flowing liquid packaged in a single use, 0.5 gram high density polyethylene ampoule with an attachable polypropylene applicator tip. It is applied as a liquid monomer to the bonding surface of the cap/crown to be cemented to a tooth. Upon seating the cap/crown and contact with weak bases LiguiBand® Dental polymerizes as to form a strong adhesive bond between the cap/crown and tooth.

The provided text describes a 510(k) submission for the LiquiBand® Dental adhesive, comparing it to a predicate device, OCTYLDENT® Dental Adhesive. The submission focuses on demonstrating substantial equivalence rather than establishing new acceptance criteria or conducting extensive clinical trials as would be common for novel devices.

Therefore, many of the typical elements of acceptance criteria and study design (like sample sizes for test and training sets, expert qualifications, MRMC studies, or standalone performance) are not detailed because they were not the focus of this type of regulatory submission.

Here's an analysis based on the information provided, with an emphasis on what is present and what is absent:

Acceptance Criteria and Device Performance

The core acceptance criterion for a 510(k) submission is substantial equivalence to a predicate device. This is demonstrated by showing similar intended use, materials, application methods, and performance in nonclinical testing.

Study Details for Substantial Equivalence

This is not a study to prove acceptance criteria for a novel device, but rather a submission to prove substantial equivalence to a legally marketed predicate device.

1. Sample sizes used for the test set and the data provenance: Not specified. The "test set" in this context refers to the samples used in the nonclinical chemical, physical, and biocompatibility testing. The document does not provide details on the number of samples tested for each assay or their provenance beyond being part of the manufacturer's testing. It is retrospective testing of the developed device.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable for a 510(k) nonclinical testing submission. No experts were used to establish "ground truth" in the clinical sense, as no clinical human data is presented or implied for the substantial equivalence claim. The "ground truth" or reference for the comparative nonclinical tests would be the established properties of the predicate device.

3. Adjudication method for the test set: Not applicable. There was no clinical trial or expert review panel described.

4. 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. This is not an AI/medical image analysis device.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is a medical device (dental adhesive), not an algorithm. The nonclinical tests conducted can be considered "standalone" in that they assess the device's inherent physical, chemical, and biological properties without human intervention during the "performance" phase of the test itself.

6. The type of ground truth used:

   • For the nonclinical testing (chemical, physical, biocompatibility): The "ground truth" was implicitly established by comparing the results of LiquiBand® Dental adhesive to the known, established properties and performance of the legally marketed predicate device, OCTYLDENT® Dental Adhesive. The predicate device's existing regulatory status and performance data serve as the benchmark.
   • For the intended use, material, and application method comparison: These were directly compared to the stated characteristics of the predicate device.

7. The sample size for the training set: Not applicable. This device is not an AI/machine learning model, so there is no concept of a "training set."

8. How the ground truth for the training set was established: Not applicable.

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SuperSkin® helps to protect skin exposed to irritation from moisture such as sweat, urine, and digestive juices.

SuperSkin® can also be used on unbroken skin surfaces that are exposed to friction and shear.

Do not apply over broken skin.

SuperSkin® is a topically-applied skin protectant for use on unbroken skin. The patient applies between 2-4 drops of SuperSkin® to the intact skin. The liquid polymerizes to form a thin, flexible coating.

The provided document is a 510(k) summary for a skin protectant device called SuperSkin®. It describes the device, its intended use, and technological characteristics, as well as the types of data submitted to the FDA to demonstrate substantial equivalence to predicate devices. However, it does not explicitly detail specific acceptance criteria or a dedicated study report comparing its performance against such criteria. The document focuses on demonstrating that SuperSkin® provides equivalent performance to predicate devices in areas like moisture barrier, vapor penetration, shear protection, friction reduction, and chronic irritation.

Therefore, much of the requested information cannot be directly extracted from this document. I will fill in what can be inferred and state when information is not present.

Here's an attempt to answer the questions based on the provided text, while acknowledging limitations:

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

The document doesn't provide a table of explicit, quantified acceptance criteria in the format typically used for medical device performance studies alongside reported device performance. Instead, it states that the device provides "equivalent performance" to predicate products across several characteristics.

Note: The "acceptance criteria" listed above are inferred from the technological characteristics and data submitted, which state the device "demonstrates appropriate..." and "provides equivalent performance..." to predicate devices. No specific quantitative thresholds are provided for these equivalency claims.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: The document does not specify the sample size used for the clinical model for friction reduction, moisture protection, or chronic irritation. It only states these were "evaluated in a clinical model" or "in a chronic irritation setting."
• Data Provenance: The country of origin of the data is not specified. The study appears to be prospective, given it describes "clinical model" and "chronic irritation setting" evaluations.

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

This information is not provided in the document. The studies mentioned (e.g., chronic irritation setting, clinical model for friction reduction and moisture protection) do not describe an expert-based ground truth establishment process, nor the number or qualifications of any experts involved.

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

The document does not describe any adjudication method for establishing ground truth for a test set. This type of detail is typically found in studies involving expert interpretation, which is not the primary focus of this 510(k).

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

• MRMC Study: No, an MRMC comparative effectiveness study was not done. The device is a physical skin protectant, not an AI-assisted diagnostic or interpretive tool.
• Effect size of human readers with/without AI: This is not applicable as the device is not an AI-based system.

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

This is not applicable as the device is a physical product, not an algorithm. Its performance is inherent to its physical properties and interaction with the skin.

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

The term "ground truth" as typically understood in AI/imaging studies does not directly apply here. Instead, the performance evaluations relied on:

• ISO 10993 guidelines: For biological testing of medical materials (for biocompatibility and non-toxicity).
• Clinical model evaluations: For friction reduction and moisture protection.
• Chronic irritation setting: For safety and irritation potential.

These likely involved objective measurements based on established standards and clinical observation/assessment rather than expert consensus on interpretive data or pathology. Outcomes data specific to patients (beyond irritation assessment) are not mentioned.

8. The sample size for the training set

This is not applicable. The device is a physical product, not an AI model that requires a training set.

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

This is not applicable as there is no training set for a physical device.

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