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Pyrenees and Blue Ridge Cervical Plate Systems are intended for use in anterior screw fixation to the cervical spine (C2-T I ) for the following indications: degenerative disease (DDD), spondylolisthesis, trauma (including fractures), spinal stenosis and tumors (primary and metastatic), failed previous fusions (pseudarthrosis) and deformity (defined as scoliosis, kyphosis or lordosis).

The Pyrenees Cervical Plate System consists of plates (1-5 levels) and screws (self-tapping and self-starting) made of titanium in accordance with ASTM F1472 and F136 and CP titanium per ASTM F67. The Pyrenees Cervical Plates are offered in both constrained and translational designs. The subject 510(k) adds plates and screws to the system. The Pyrenees system components are provided non-sterile.

Function: The system functions as a spinal fixation device to provide support and stabilization of the cervical vertebrae.

The provided text describes the Pyrenees Cervical Plate System, a spinal implant, and its substantial equivalence to predicate devices, but it does not contain information related to a study proving the device meets acceptance criteria in the context of an AI/ML medical device, which is what your request implies by asking for details like "sample size used for the test set," "number of experts used to establish the ground truth," or "MRMC comparative effectiveness study."

The document is a K510(k) summary for a traditional medical device (spinal plate system) seeking premarket notification clearance, not an AI/ML device. Therefore, the "acceptance criteria" and "study" described here are related to the mechanical performance of the physical implant, not diagnostic or predictive performance of an algorithm.

Here's a breakdown of what is provided, and what is missing based on your prompt, assuming you are asking about an AI/ML context:

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

• Note: These are mechanical performance criteria for a physical device, not diagnostic performance metrics (e.g., sensitivity, specificity, AUC) for an AI/ML system.

Missing Information (as per your AI/ML specific questions):

• 2. Sample size used for the test set and the data provenance: Not applicable. The "test set" here refers to the physical devices subjected to mechanical testing, not a dataset for an AI/ML model.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI/ML model (e.g., disease presence) is not relevant for a mechanical test on a physical implant.
• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not 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: Not applicable. This device is not an AI algorithm assisting human readers.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable. The "ground truth" for mechanical testing is established by test standards and physical measurements.
• 8. The sample size for the training set: Not applicable. This is a physical device, not an AI model requiring a training set.
• 9. How the ground truth for the training set was established: Not applicable.

Summary based on the provided document:

The Pyrenees Cervical Plate System underwent mechanical testing to demonstrate its safety and effectiveness. The study involved comparing its mechanical performance (static compression, static torsion, dynamic compression) against predicate devices according to ASTM F1717. The conclusion was that the new system performed "equally to or better than" the predicate devices in these tests, thus meeting the criteria for substantial equivalence. The document does not describe studies or criteria relevant to AI/ML device performance.

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