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The Rampart™ D Lumbar Interbody Fusion Device is indicated for intervertebral body fusion at one level or two contiguous levels in the lumbar spine from L2 to L5 in patients with degenerative disc disease (DDD) with up to Grade I spondylolisthesis at the involved level(s). DDD is defined as back pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies. These patients should be skeletally mature and have had six months of non-operative treatment. The Rampart D device is designed for use with autograft and/or allograft comprised of cancellous and/or corticocancellous bone graft as an adjunct to fusion and is intended for use with supplemental fixation systems cleared by the FDA for use in the lumbar spine.

The Rampart™D Lumbar Interbody Fusion Device is an intervertebral implant designed to provide mechanical support within the intradiscal space as an adjunct to fusion. The device is made of PEEK-OPTIMA® LT-1, titanium alloy, polyethylene terephthalate (PET), and tantalum markers. It is available in varying lengths and heights with two lordotic configurations, and is provided sterile. It is designed with a porous central cavity for graft containment. The device features a rounded nose to aid implant insertion and includes ridged teeth to resist migration.

This document is a 510(k) premarket notification for a medical device called the "Rampart™ D Lumbar Interbody Fusion Device." It is not an AI/ML device, and therefore the information requested about acceptance criteria and study proving adherence to criteria in the context of AI/ML are not applicable here.

This document describes a traditional medical device (an interbody fusion device) and demonstrates its "substantial equivalence" to legally marketed predicate devices, as required for 510(k) clearance by the FDA. The "testing" section refers to non-clinical (bench) testing and patient-level clinical data supplemented by a literature review to support this substantial equivalence.

Therefore, the requested information which pertains to AI/ML device evaluation criteria, such as "number of experts used to establish ground truth," "adjudication method for the test set," "MRMC comparative effectiveness study," "standalone performance," "sample size for training set," and "how ground truth for training set was established," are not relevant to this type of device submission and are not found in the provided text.

The "acceptance criteria" for this device are meeting the performance standards of the predicate devices through the non-clinical and clinical testing mentioned, demonstrating it is "substantially equivalent" in safety and effectiveness.

Here's a breakdown of the relevant information from the document:

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

Since this is a non-AI/ML medical device, the "acceptance criteria" are not based on metrics like sensitivity, specificity, or AUC, but rather on demonstrating substantial equivalence to a predicate device through various physical and mechanical tests, and clinical data. The document states:

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

• Test Set (Clinical Data): "Patient-level clinical data" was provided, but the specific sample size, country of origin, or whether it was retrospective or prospective, is not detailed in this 510(k) summary. This information would typically be in the full submission, not in this publicly accessible summary.
• Bench Testing: "All testing was conducted on worst case configurations for both sizing and recommended graft fill." No specific numerical sample sizes for non-clinical (bench) tests are provided in this summary.

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 is not applicable to this device type. Ground truth in the context of an AI/ML device refers to human expert annotations. For a traditional medical device, clinical data (patient outcomes, imaging findings relevant to the DDD diagnosis, etc.) forms part of the supporting evidence, but there's no "ground truth" derived from expert consensus in the same way as for an AI/ML diagnostic algorithm.

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

• Not applicable for this traditional medical device.

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 as this is not an AI/ML device assisting human readers.

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

• Not applicable as this is a physical medical implant, not an algorithm.

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

• For the clinical support, it refers to "patient history and radiographic studies" for the diagnosis of Degenerative Disc Disease (DDD) and overall "patient-level clinical data." The "ground truth" for the device's performance would ultimately be patient outcomes related to successful fusion and safety, compared to the predicate device. However, the details are not provided in this summary.

8. The sample size for the training set:

• 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:

• Not applicable as this is not an AI/ML device.

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The ReGen Collagen Scaffold (CS) is intended for use in surgical procedures for the reinforcement and repair of soft tissue injuries of the medial meniscus. In repairing and reinforcing medial meniscal defects, the patient must have an intact meniscal rim and anterior and posterior horns for attachment of the mesh. In addition, the surgically prepared site for the CS must extend at least into the red/white zone of the meniscus to provide sufficient vascularization.

The CS reinforces soft tissue and provides a resorbable scaffold that is replaced by the patient's own soft tissue. The CS is not a prosthetic device and is not intended to replace normal body structure.

The ReGen Collagen Scaffold (CS) is a resorbable collagen matrix comprised primarily of bovine type I collagen. The CS is provided in a semi-lunar shape with a triangular cross section to be used to reinforce weakened soft tissue and provide a resorbable scaffold that is replaced by the patient's own tissue. The surgeon trims the device to the size necessary for repair of the damaged or weakened soft tissue.

This document is a 510(k) summary for a medical device (ReGen Collagen Scaffold (CS)), not a study report detailing acceptance criteria and performance. Therefore, most of the requested information regarding study details, sample sizes, and expert qualifications cannot be extracted directly from this document. This document primarily focuses on establishing substantial equivalence to predicate devices.

However, based on the text provided, I can infer and state the following:

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

The document does not explicitly state quantitative acceptance criteria or a direct table of reported device performance against such criteria. The "performance" is implicitly demonstrated through the claim of substantial equivalence to predicate devices based on biomechanical, biocompatibility, animal testing, and clinical studies.

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

• Test Set Sample Size: The document mentions a prospective, randomized multicenter clinical trial that had two separately controlled and randomized arms:
  • Acute arm: 157 patients with no prior surgery to the involved meniscus.
  • Chronic arm: 154 patients with one to three prior treatments to the involved meniscus.
  • Total patients: 311.
• Data Provenance: The study was a "multicenter clinical trial." The document does not explicitly state the country of origin, but given the submission to the FDA in the US, it is likely that the data originated from the US or included US sites. The trial was prospective and randomized.

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 available in the provided text. The document refers to a clinical trial but does not detail how the "ground truth" (e.g., success or failure of the repair) was established by experts, nor does it specify the number or qualifications of such experts.

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

This information is not available in the provided text.

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

This is not applicable. The device is a "Collagen Scaffold" (surgical mesh), not an AI or imaging diagnostic device that would involve human readers or AI assistance.

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

This is not applicable for a physical surgical mesh device.

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

Given that it was a clinical trial for a surgical mesh, the "ground truth" would likely be based on clinical outcomes data related to the reinforcement and repair of soft tissue injuries of the medial meniscus. This would typically involve measures of healing, function, pain, and potentially re-injury rates over the follow-up period.

8. The sample size for the training set

This is not applicable. For a physical medical device like a surgical mesh, there isn't typically a "training set" in the context of machine learning. The clinical trial data served as the primary evidence.

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

This is not applicable for a physical medical device.

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