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Over-The-Counter Indications: For minor burns, abraded skin, irritated areas and minor wounds. Professional Prescription Indications: Wound moistening solution for dermal lesions such as Stage I-IV pressure ulcers, stasis ulcers, foot ulcers, diabetic ulcers, post surgical wounds, first and second degree burns, abrasions and skin irritations.

A buffered ionic silver aqueous wet dressing for external wound management. The product provides a moist environment that aids the body in the healing process. The non-irritating components of Silvion Antibacterial Silver Skin & Wound Moisturizing Solution allow the product to be used on mucous membranes, the skin as well as acute and chronic wounds. The silver provides effective protection of the solution against microbial contamination. The Silvion Antibacterial Silver Skin & Wound Moisturizing Solution is clear, odorless and colorless. The product is available in 2 oz, 4 oz, 8 oz and 16 oz spray bottles.

The provided text describes a 510(k) premarket notification for the "Silvion™ Antibacterial Silver Skin & Wound Moisturizing Solution." This document is a regulatory submission to the FDA to demonstrate substantial equivalence to legally marketed predicate devices, not a study designed to establish acceptance criteria for a new device's performance against predefined metrics.

Therefore, the requested information about acceptance criteria, device performance, sample sizes, ground truth, expert involvement, and comparative effectiveness studies is not available in the provided text because this type of detailed performance study is not typically included in a 510(k) summary for a product like this, which relies on demonstrating equivalence rather than establishing new performance benchmarks.

The document primarily focuses on:

• Device Description: What the product is and its components.
• Intended Use: The conditions it's meant to treat (minor burns, wounds, ulcers, etc.).
• Predicate Devices: Other similar products already on the market that it claims to be substantially equivalent to.
• Assessment of Performance Data: This section mentions various in vitro and in vivo biocompatibility, cytotoxicity, and antimicrobial tests. These tests are conducted to demonstrate the safety and fundamental antimicrobial properties (as a drug component) and biocompatibility (as a device component) that support its substantial equivalence to other wound care products, not to meet specific performance acceptance criteria against a defined clinical outcome.

To directly answer your request based on the provided text, the specific information regarding acceptance criteria and the detailed study design to prove device performance as typically expected for advanced AI/medical device performance evaluation is not present.

Here's an attempt to fill in the table and explain the missing information based on the presented content:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample size for the test set: Not specified in the document. The document refers to various in vitro and in vivo tests, which would have their own sample sizes (e.g., for animal studies in biocompatibility, or microbial cultures for antimicrobial tests). However, these are not detailed.
• Data provenance: The studies mentioned (biocompatibility, cytotoxicity, antimicrobial) are typically conducted under controlled laboratory conditions, implying they are proprietary data generated for the submission. The country of origin and whether they are retrospective or prospective are not stated.

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

• This information is not applicable and not provided in this type of submission. Ground truth and expert consensus are relevant for AI/diagnostic imaging devices where human interpretation is being compared or augmented. For a topical wound care solution, performance is typically assessed through laboratory tests and animal/human safety studies, not expert consensus on diagnostic images.

4. Adjudication method for the test set

• Not applicable and not provided. Adjudication methods (like 2+1) are used to resolve disagreements among human readers in diagnostic studies. This is not relevant to the types of tests described for this wound care solution.

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, this was not done. An MRMC study is designed for diagnostic devices, particularly those involving human interpretation (e.g., radiologists reading images with or without AI assistance). This device is a topical wound care solution with both device and drug components; it is not a diagnostic tool that enhances human reader performance.

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

• Not applicable. This device is a physical solution applied to the skin, not an algorithm.

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

• For the biocompatibility studies, the "ground truth" would be established by standard ISO and USP guidelines for determining irritation, sensitization, and toxicity. This involves histological examination, clinical observation of reactions, and survival rates in animal models.
• For the cytotoxicity testing, the "ground truth" would be the observed cytotoxic effect on cells as measured by the Agarose Overlay Method.
• For the antimicrobial testing, the "ground truth" would be the measurable reduction or inhibition of microbial growth according to established microbiologic test methods (e.g., MIC, MBC, Zone of Inhibition, USP tests).
• For demonstrating substantial equivalence, the "ground truth" is the performance and safety profile of the legally marketed predicate devices.

8. The sample size for the training set

• Not applicable and not provided. The concept of a "training set" is relevant for machine learning algorithms. This device is a chemical formulation, not an AI model.

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

• Not applicable. As there is no training set for an algorithm, there is no ground truth established for a training set.

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