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This device is intended for implantation to reinforce soft tissue including, but not limited to: defects of the abdominal and thoracic wall, muscle flap reinforcement, prolapse repair, reconstruction of the pelvic floor, hernias, suture-line reinforcement and reconstructive procedures. The device is intended for one-time use.

Xylos® Porous Surgical Mesh is a sterile, non-resorbable product composed of solvent-dehydrated, microbial-derived cellulose. The implantable unit is presented in sterile double-pouched packages for appropriate removal in preparation for surgery.

The provided text describes a 510(k) premarket application for the Xylos® Porous Surgical Mesh, which is a modification of a previously cleared device. The "study" referenced is an evaluation of this modification, rather than a clinical trial in the traditional sense, focusing on establishing substantial equivalence to the predicate device.

Here's an analysis based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

The submission states, "The verification and validation tests that were performed confirmed that the Xylos® Porous Surgical Mesh met the acceptance criteria. Therefore the device should perform as intended." However, it does not explicitly list the specific acceptance criteria or the numerical performance results from these tests.

The primary "acceptance criterion" demonstrated in this 510(k) summary is that the additional perforation processing step does not adversely affect the safety or effectiveness of the device compared to the predicate. The performance in this context is deemed acceptable if this equivalence is maintained.

2. Sample Size Used for the Test Set and Data Provenance

The document refers to "verification and validation tests" but does not specify the sample size used for these tests.
The data provenance is not explicitly detailed but would likely originate from laboratory and engineering testing conducted by Xylos Corporation, not from patient data (retrospective or prospective) in a clinical setting.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This type of information is not applicable to this 510(k) submission. "Ground truth" established by human experts, especially for clinical endpoints, is generally associated with clinical trials or diagnostic device evaluations. This submission focuses on engineering and material characteristics to demonstrate substantial equivalence for a manufacturing process change.

4. Adjudication Method for the Test Set

This information is not applicable for the same reasons as point 3. Adjudication methods are relevant in clinical studies where expert consensus is needed to resolve discrepancies in diagnoses or assessments.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

No, an MRMC comparative effectiveness study was not done. This device is a surgical mesh, not an AI-powered diagnostic or assistive tool for human readers.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

No, this concept is not applicable as the device is a physical surgical mesh, not an algorithm.

7. The Type of Ground Truth Used

For this specific submission, the "ground truth" would be established through engineering specifications, material science standards, and established test methodologies to evaluate the physical and mechanical properties of the mesh with the new perforation process. This likely includes:

• Mechanical Testing Results: Tensile strength, burst strength, pore size, etc.
• Biocompatibility Testing: According to ISO standards.
• Sterility Assurance: Validation of the gamma irradiation process.

It is not expert consensus, pathology, or outcomes data in a clinical sense for this particular submission, as it's not a de novo device or a major change requiring new clinical data.

8. The Sample Size for the Training Set

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

9. How the Ground Truth for the Training Set Was Established

This concept is not applicable for the same reason as point 8.

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