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The Paragon Lens Carrier is indicated for the chemical disinfection and storage of Rigid Gas Permeable and Hard contact lenses.

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This document is a 510(k) summary for the Paragon Lens Carrier, a contact lens case. It is important to note that this document does not describe an AI/ML medical device. Therefore, many of the requested categories are not applicable.

Here's an analysis based on the provided text:

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

Since this is not an AI/ML device, typical performance metrics like sensitivity, specificity, or AUC are not applicable. The device is a contact lens carrier, and its "performance" is implicitly tied to safety and substantial equivalence to a predicate device.

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

This information is not provided. For a physical device like a contact lens case, "test set" typically refers to the number of units manufactured and tested for toxicology, sterility, or durability. The document only mentions toxicology testing was submitted, but not the specifics of the sample size or provenance.

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)

Not applicable. "Ground truth" in this context would relate to the scientific methods and regulatory standards for toxicology testing, not expert consensus on medical images or diagnoses.

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

Not applicable. This is not an AI/ML diagnostic device that requires adjudication of expert opinions for a 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 is a physical contact lens case, not an AI-assisted diagnostic tool.

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

Not applicable. This is a physical contact lens case, not an algorithm.

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

The "ground truth" for the safety of the device would be established through toxicological testing results following recognized scientific and regulatory standards. For substantial equivalence, the "ground truth" is a comparison against the predicate device's design, materials, and intended use as per FDA regulations.

8. The sample size for the training set

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

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

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

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