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Optiband LC Band Cement is a light-cured orthodontic bonding cement that is intended to be used for the attachment of orthodontic appliances to teeth.

The device is a resiri-based fluoride releasing band cement. It is a single paste so messy powder and liquid mixing is eliminated as well as inconsistent mixing of catalyst/activator type cements. Optiband LC achieves high strength following light cure allowing active archwires to be placed immediately. Optiband LC has higher tensile and compressive strength than glass ionomers or zinc phosphates and does not have an unpleasant taste or odor. It is specially formulated to have high adhesion to enamel and metal surfaces to reduce the chances of wash-out and loose bands. Optiband LC is offered in two shades, chromatic formula and blue formula.

Here's an analysis of the provided text regarding the acceptance criteria and supporting study for the Optiband LC device, presented in the requested format.

It's important to note that the provided text is a 510(k) summary for a dental adhesive, which focuses on substantial equivalence to a predicate device rather than a comprehensive clinical study proving specific performance metrics against pre-defined acceptance criteria with detailed statistical analysis as one might find for a more complex medical device or AI algorithm.

Therefore, many of the requested sections (like sample sizes for test/training sets, expert qualifications, MRMC studies, standalone performance of an algorithm, etc.) are not applicable or not explicitly detailed in this type of regulatory submission for this particular device. The "study" here is primarily a comparison of material properties and intended use to a legally marketed predicate.

Acceptance Criteria and Device Performance for Optiband LC

1. Table of Acceptance Criteria and Reported Device Performance

Explanation of "Acceptance Criteria" for this device: For a 510(k) submission of a dental cement, acceptance criteria are generally established by demonstrating that the new device is "substantially equivalent" to an already legally marketed predicate device. This typically involves performance bench testing (e.g., tensile strength, compressive strength, bond strength) and material characterization (e.g., rheology, cure depth, fluoride release) to show that the new device performs as well as or better than the predicate for its intended use, and does not raise new questions of safety or effectiveness. The text explicitly states "higher tensile and compressive strength than glass ionomers or zinc phosphates," implying these are baseline performance requirements that the device exceeds. The comparison with Transbond Plus is for overall "substantial equivalence."

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

• Sample Size: Not specified in the provided 510(k) summary. For a dental material, this would typically involve a certain number of samples tested for each material property (e.g., N=10 for bond strength, N=5 for compressive strength).
• Data Provenance: Not explicitly stated as retrospective or prospective, nor the country of origin. This would typically be laboratory bench testing conducted by the manufacturer.

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

• Not Applicable. This is a material science study of a dental adhesive, not an image analysis or diagnostic AI device where human experts establish ground truth for a test set. Performance metrics are derived from instrumental measurements (e.g., forcemeasurement devices for strength, spectrometers for material composition).

4. Adjudication Method for the Test Set

• Not Applicable. As this is not an expert-driven assessment, there is no adjudication method. Results are quantitative measurements from lab tests.

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 is a dental material, not an AI or imaging device involving human readers.

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

• Not Applicable. This describes a physical dental material, not an algorithm.

7. The Type of Ground Truth Used

• The "ground truth" for verifying the device's performance would be established through objective, standardized laboratory measurements of its physical and chemical properties (e.g., tensile strength, compressive strength, bond strength, fluoride release, setting time, viscosity) performed according to recognized test methods (e.g., ISO standards, ADA specifications). These are quantitative, instrumental measurements, not expert consensus or outcomes data in the usual clinical trial sense.

8. The Sample Size for the Training Set

• Not Applicable/Not Explicitly Stated. For a traditional medical device like this, there isn't a "training set" in the context of machine learning. If one were to interpret "training set" as the data used during product development and formulation optimization, it would involve numerous experimental batches and tests, but a specific "sample size" for "training" is not a standard concept for this type of device and would not be reported in a 510(k) summary.

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

• Not Applicable/Not Explicitly Stated. Similar to point 8, the "ground truth" during development would be the desired performance characteristics identified through scientific literature, predicate device analysis, and dental professional feedback. The "establishment" would be through iterative formulation, material characterization, and bench testing, aiming to achieve and optimize these desired properties.

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