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Serves as a desensitizer/preventative in dentin hypersensitivity conditions by Serves as a decomorialized proventual tubules which effectively blocks the fluid flow in the tubules that cause sensitivity.

GLUMA ONE BOND is a one bottle adhesive, designed to bond resinous restorative materials to dental hard tissue. The product is a light curing monomer mixture dissolved in acetone which, following conditioning with phosphoric acid, is applied to enamel and dentin prior to co- polymerization with the restorative. The composition of GLUMA ONE BOND is based on three monomers which individually have been used in marketed dental products for decades. The hydrophilic hydroxyethylmethacrylate (HEMA) is essential for perfect wetting of and penetration into the conditioned tooth structure. Similarly, 4-MET(A) has hydrophilic and moieties for wetting and polymerization, whereas the urethane di-methacrylate monomer is responsible for formation of a cross-linked polymer network. The organic solvent acetone has the function of a carrier for the monomers while being an effective water chaser at the same time.

The provided text describes a dental adhesive product called GLUMA ONE BOND, but it does not contain information about acceptance criteria or a study that proves the device meets specific acceptance criteria as would be expected for a diagnostic AI/ML device.

The document is a 510(k) submission for a medical device (a dental bonding agent) and an FDA clearance letter. It describes the product's composition, intended use, and briefly mentions:

• "Thorough in-vitro investigation has proven that the GLUMA ONE BOND performs better than or at least as good as the leading resinous bonding systems available in the market."
• "The results of a short-term clinical observation done by eight dentists under regular office conditions, on some more than 300 restorations bonded with GLUMA ONE BOND confirms that the product is easy to use, safe, and effective."

This information is insufficient to answer the detailed questions regarding acceptance criteria and study design for an AI/ML device. The request specifically asks about "acceptance criteria and the study that proves the device meets the acceptance criteria" in the context of an AI/ML device, referencing concepts like test sets, ground truth, experts, and comparative effectiveness studies, which are not applicable to the GLUMA ONE BOND description.

Therefore, for the requested information:

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

• Cannot be provided. The text does not specify quantitative acceptance criteria (e.g., sensitivity, specificity, AUC) or detailed performance metrics relevant to an AI/ML device. It only makes general statements about performing "better than or at least as good as" leading systems and being "easy to use, safe, and effective."

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

• Test set sample size: "some more than 300 restorations" were observed clinically. This refers to a clinical observation, not a specific test set for an AI/ML algorithm.
• Data provenance: Not specified. It was "under regular office conditions" but no country or retrospective/prospective nature is detailed.

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):

• Number of experts: "eight dentists" conducted the clinical observation.
• Qualifications: "dentists" is the only qualification mentioned. Specific experience or specialization is not provided.
• Ground truth establishment: Not applicable in the context of an AI/ML device's test set. The clinical observation focused on user experience (easy to use) and general safety/effectiveness of bonding, not on establishing a diagnostic ground truth.

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

• Cannot be provided. Adjudication methods are relevant for establishing ground truth in diagnostic studies, which is not described here.

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. An MRMC comparative effectiveness study is not described. The product is a dental adhesive, not an AI/ML diagnostic tool.

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

• No. This question is not applicable as the product is not an AI algorithm.

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

• For the clinical observation, the "ground truth" was likely subjective assessment by the dentists regarding ease of use, safety, and effectiveness of the bonding performance, rather than a formal, objective ground truth like pathology for a diagnostic device. For the in-vitro studies, the "ground truth" would be objective measurements of bond strength and durability in a lab setting, but specifics are not given.

8. The sample size for the training set:

• Not applicable. This is not an AI/ML device that requires a training set.

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

• Not applicable. This is not an AI/ML device that requires a training set.

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GLUMA ONE BOND is a light curing single component enamel/dentin adhesive for onoming of all current brands of resin composite restorative materials and compomers. It also enhances adhesion to metals such as amalgam. GLUMA ONE BOND combines primer and adhesive in single bottle.

GLUMA ONE BOND is a one bottle adhesive, designed to bond resinous restorative materials to dental hard tissue. The product is a light curing monomer mixture dissolved in acetone which, following conditioning with phosphoric acid, is applied to enamel and dentin prior to co- polymerization with the restorative. The composition of GLUMA ONE BOND is based on three monomers which individually have been used in marketed dental products for decades. The hydrophilic hydroxyethylmethacrylate (HEMA) is essential for perfect wetting of and penetration into the conditioned tooth structure. Similarly, 4-MET(A) has hydrophilic and moieties for wetting and polymerization, whereas the urethane di-methacrylate monomer is responsible for formation of a cross-linked polymer network. The organic solvent acetone has the function of a carrier for the monomers while being an effective water chaser at the same time.

This document is a 510(k) submission for a dental adhesive, GLUMA ONE BOND, and therefore does not contain acceptance criteria or study details as typically found in medical device performance reports with quantitative metrics. The provided text is focused on demonstrating substantial equivalence to a predicate device, a regulatory pathway often used for low-risk devices.

Therefore, many of the requested categories for a rigorous performance study cannot be filled from the provided text.

Here's an attempt to extract what information is available and explain the limitations:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size:
  • In-vitro investigation: Not specified. The document only states "Thorough in-vitro investigation."
  • Clinical observation: "some more than 300 restorations."
• Data Provenance:
  • In-vitro investigation: Not specified (likely laboratory setting).
  • Clinical observation: Conducted by eight dentists "under regular office conditions," implying a prospective, real-world, clinical setting. The country of origin is not specified but given the submitter's location (Indiana, USA) and the FDA review, it is likely US-based or at least compliant with US standards.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

• In-vitro investigation: Not specified.
• Clinical observation: Eight dentists. Their specific qualifications (e.g., years of experience, specialization) are not detailed beyond being "dentists."

4. Adjudication Method for the Test Set

• Not applicable/Not specified. The clinical observation involved individual dentists assessing their own restorations, rather than a separate adjudication process for a centralized test set.

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. This device is a dental adhesive, not an AI-powered diagnostic or assistive tool for human readers. Therefore, an MRMC study related to AI assistance is irrelevant to this submission.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Not applicable. This is a material device, not an algorithm. Its performance is inherent to the chemical and physical properties of the adhesive.

7. The Type of Ground Truth Used

• In-vitro investigation: Likely laboratory-based measurements of bond strength, durability, and other material properties (though specific metrics are not detailed).
• Clinical observation: Clinical outcomes (e.g., success/failure of restorations, ease of use feedback) as observed by the treating dentists. This is a form of expert observation/outcome data.

8. The Sample Size for the Training Set

• Not applicable. As a physical product, there is no "training set" in the context of an AI algorithm. Its development is based on chemical formulation and in-vitro/in-vivo testing.

9. How the Ground Truth for the Training Set Was Established

• Not applicable. This is a manufacturing process and product development cycle, not an AI model development. The "ground truth" for its development would be established through chemical research, engineering principles, and iterative testing/formulation.

Summary of Limitations:

This 510(k) submission, particularly one from 1998 for a dental adhesive, predates modern detailed reporting requirements for AI/diagnostic devices. The information provided aligns with typical substantial equivalence claims for physical products, focusing on comparative performance against existing products and general clinical observations. It does not contain the granular detail about study design, statistical methods, or expert qualifications that would be expected for a complex diagnostic or AI-driven system today.

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