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FOR TOPICAL DERMATOLOGICAL USE ONLY

EPICERAM® is a skin barrier emulsion to be used to treat dry skin conditions and to manage and relieve the burning and itching associated with various types of dermatoses, including atopic dermatitis, irritant contact dermatitis, radiation dermatitis. EPICERAM® helps to relieve dry waxy skin by maintaining a moist wound & skin environment, which is beneficial to the healing process.

Apply Epiceram® in a thin layer to the affected skin areas 2 times per day (or as needed) and massage gently into the skin. If the skin is broken, cover Epiceram® with a dressing of choice.

EPICERAM™ is a non-sterile, viscous, lipid-rich emulsion presented for prescription use.

The provided text focuses on the 510(k) submission for the EPICERAM® Skin Barrier Emulsion and its substantial equivalence to predicate devices. It does not contain information about acceptance criteria, specific device performance metrics, study details (sample sizes, data provenance, ground truth establishment, expert qualifications, or adjudication methods), or comparative effectiveness studies (MRMC).

The document states:

• "Functional and performance testing has been conducted to assess the safety and effectiveness of EPICERAM™ Skin Barrier Emulsion and the results are satisfactory." However, it does not detail what those tests were, what the acceptance criteria were, or what the specific "satisfactory" results entail.
• The FDA's letter confirms substantial equivalence based on the provided information but does not elaborate on the underlying performance data.

Therefore, I cannot fulfill most of the requested points based on the provided text.

Here's a breakdown of what can be extracted and what is missing:

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

• Cannot be provided. The document states that "Functional and performance testing has been conducted... and the results are satisfactory," but it does not list specific acceptance criteria or report quantitative device performance metrics.

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

• Cannot be provided. The document makes no mention of study design, sample sizes, or data provenance for any testing.

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)

• Cannot be provided. There is no mention of independent experts, ground truth establishment, or their qualifications.

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

• Cannot be provided. There is no mention of any adjudication method.

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

• Cannot be provided. This document is for a topical skin emulsion, not an AI-assisted diagnostic device. Therefore, an MRMC study as described (comparing human readers with/without AI assistance) is not applicable and not mentioned.

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

• Cannot be provided. As explained above, this is not an AI/algorithm-based device.

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

• Cannot be provided. There is no information regarding how "satisfactory" results were defined or validated, or what type of ground truth (if any formal one was used beyond clinical observation) was applied.

8. The sample size for the training set

• Cannot be provided. This is not an AI/machine learning device that would require a "training set."

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

• Cannot be provided. Not applicable for this type of device.

In summary, the provided 510(k) summary focuses on demonstrating substantial equivalence to existing predicate devices for a topical skin barrier emulsion, not on detailed clinical trial results with specific acceptance criteria or performance metrics typically seen for diagnostic devices or AI algorithms.

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