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The V.A.C. GranuFoam Silver Dressing is indicated as an effective barrier to bacterial penetration for patients who would benefit from the V.A.C. family of negative pressure devices to help promote wound healing. The barrier functions of the dressing may help reduce infection in chronic, acute, traumatic, subacute and dehisced wounds, diabetic ulcers, pressure ulcers, flaps, grafts, and partial burns.

The V.A.C.® GranuFoam™ Silver dressing is a modification to the V.A.C. product line of black, reticulated, polyurethane foam dressings, designed specifically for use with the V.A.C. family of feedback-controlled devices, including the miniV.A.C. 9, V.A.C.® Freedom™ and V.A.C.® ATS™ systems, all currently marketed by KCI under 510(k) K032310. The modification is the addition of a silver containing coating to the GranuFoam™ dressing to allow for a silver option to the dressing line.

The provided text is a 510(k) summary for the V.A.C.® GranuFoam™ Silver Dressing. It does not describe an AI/ML device or a study proving its performance against specific acceptance criteria in the way typically expected for such devices (e.g., sensitivity, specificity, AUC). Instead, this document focuses on demonstrating substantial equivalence to predicate devices for a medical dressing, primarily through bench testing for biocompatibility and in-vitro antimicrobial effectiveness.

Therefore, many of the requested categories are not applicable to this document. I will fill in the relevant information and indicate where information is not present or not applicable in the context of this type of medical device submission.

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

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

• Sample Size: Not specified in the document for any testing.
• Data Provenance: Not specified, but tests were conducted by "licensed commercial reference laboratories" under GLP studies (Good Laboratory Practice), suggesting adherence to established scientific and regulatory standards. The 510(k) summary originated from Kinetic Concepts, Inc. in San Antonio, TX, USA, suggesting the data is likely from or overseen by US entities.

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. The ground truth for biocompatibility and antimicrobial effectiveness would be established through laboratory testing and standardized assays, not expert consensus in the clinical sense.

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

Not applicable. Adjudication methods like 2+1 or 3+1 are typically used for clinical and often image-based studies involving human interpretation. The reported studies are bench and in-vitro 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 device is a medical dressing, not an AI/ML diagnostic tool, so an MRMC study is irrelevant.

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

Not applicable. This device is a medical dressing, not an algorithm.

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

• Biocompatibility: Established through standardized GLP (Good Laboratory Practice) studies in accordance with ISO-10993 for surface devices in contact with breached or compromised surfaces. This involves specific assays to determine toxicity, irritation, and sensitization. The "ground truth" is adherence to these established safety norms determined by the test results.
• Antimicrobial Effectiveness: Established through "separate in-vitro laboratory evaluations" using "licensed commercial reference laboratories." The "ground truth" here is the measured antimicrobial activity against specific microorganisms under controlled laboratory conditions.

8. The sample size for the training set

Not applicable. This device is a medical dressing, not an AI/ML model that requires a training set.

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

Not applicable. This device is a medical dressing, not an AI/ML model that requires a training set.

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