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The ACell UBM Lyophilized Wound Dressing is intended for the management of wounds including: partial and full-thickness wounds, pressure ulcers, venous ulcers, diabetic ulcers, chronic vascular ulcers, tunneled/undermined wounds, surgical wounds (donor sites/grafts, post-Moh's surgery, post-laser surgery, podiatric, wound dehiscence), trauma wounds (abrasions, lacerations, second-degree burns, and skin tears), and draining wounds. The device is intended for one-time use.

The ACell UBM Lyophilized Wound Dressing is primarily composed of porcine collagen and is supplied sterile in single sheet sizes ranging from 2 x 4 cm to 14 x 20 cm.

The provided text describes the 510(k) summary for the ACell UBM Lyophilized Wound Dressing. It indicates that the device was subjected to various tests to assess its biocompatibility and performance. However, it does not provide specific acceptance criteria in a table format with reported device performance metrics. Instead, it makes a general statement about passing "all tests."

Here's an attempt to answer the questions based on the available information, noting where information is missing:

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

Note: The document states, "The ACell UBM Lyophilized Wound Dressing was subjected to a number of tests to assess the biocompatibility and the performance of the material. It passed the requirements of all tests and was shown to be safe and effective as a wound dressing." However, it does not detail the specific parameters or thresholds for these "requirements" or provide quantitative data for the device's performance against them.

2. Sample size 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 in the document. The filing describes the type of tests conducted (biocompatibility, performance) but does not specify the sample sizes used for these tests, the origin of any data, or whether it was retrospective or prospective.

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)

This information is not provided in the document. The document describes testing of the material itself rather than a diagnostic or interpretive device that would require expert-established ground truth.

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

This information is not provided in the document. As the tests appear to be primarily laboratory or material-based rather than involving human interpretation, an adjudication method in the typical sense for medical imaging or similar devices would not be applicable.

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

An MRMC comparative effectiveness study was not conducted, and this device is not an AI diagnostic device. The product is a wound dressing.

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

This question is not applicable as the device is a physical wound dressing and not an algorithm or software. Its performance is inherent to its material properties and biological interaction, not through an algorithm.

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

The "ground truth" for this device would be established through laboratory testing standards and in-vivo/in-vitro biocompatibility assessments, rather than expert consensus on diagnostic images or pathology. The document generally states "passed the requirements of all tests," implying adherence to established testing protocols for safety and performance of medical devices.

8. The sample size for the training set

This information is not provided and is not applicable as this is not an AI/ML device that requires a training set.

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

This information is not provided and is not applicable as this is not an AI/ML device that requires a training set.

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