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The Creaspine SupStance Vertebral Body Replacement System, Line Extension, (SupStance VBR System) is a vertebral body replacement system intended to replace a vertebral body. The SupStance VBR System is designed for use in the thoraco-lumbar spine (T1- L5) to replace a collapsed, damaged, or unstable vertebral body during tumor or trauma (i.e., fracture) management procedures. The SupStance VBR System is intended to be used with supplemental internal fixation systems. Anterior thoracolumbar plates and screws or pedicle screw and rod systems are among the options for the surgeon to use.

The use of allograft and/or autograft with the SupStance VBR System is optional.

The proposed Creaspine SupStance VBR System, Line Extension, is a modification of the original Creaspine SupStance VBR System that was the subject of K072537. This device modification has been submitted as a Special 510(k) Premarket Notification because the proposed SupStance VBR System, Line Extension, is identical in intended use and fundamental scientific technology to the parent SupStance VBR System originally described in K072537.

The modifications made to the parent device to produce the proposed SupStance VBR System, Line Extension, are limited to the expansion of the implant (cage) dimensions available.

The provided document is a 510(k) summary for a medical device: the Creaspine SupStance Vertebral Body Replacement System, Line Extension. This document primarily focuses on establishing substantial equivalence to previously cleared devices through mechanical testing and does not describe a clinical study involving human patients or a software algorithm evaluated with clinical data.

Therefore, many of the requested items regarding acceptance criteria, device performance, sample sizes, expert ground truth, adjudication methods, MRMC studies, and standalone algorithm performance are not applicable to this type of submission.

Here's a breakdown of the information that can be extracted based on the provided text, and where certain requested information is N/A:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample size for test set: Not applicable for a software algorithm evaluated with clinical data. This submission is for mechanical testing of a physical implant. The document does not specify the number of physical devices or tests performed, only that "Performance testing included the types of mechanical testing recommended..."
• Data provenance: Not applicable. The data is generated from mechanical testing in a lab setting, not from a clinical patient population.

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

• Not applicable. This submission involves mechanical testing of a physical implant, not a clinical study requiring expert ground truth for interpretation.

4. Adjudication Method for the Test Set

• Not applicable. This submission involves mechanical testing of a physical implant, not a clinical study requiring adjudication of interpretations.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

• No. An MRMC study is not relevant for this type of device (a physical spinal implant) or the type of data presented (mechanical testing). This study does not involve human readers interpreting clinical cases with or without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was Done

• Not applicable. This device is a physical spinal implant, not a software algorithm.

7. The Type of Ground Truth Used

• For the mechanical testing, the "ground truth" would be the engineering specifications and performance benchmarks established for vertebral body replacement systems, likely based on relevant standards and predicate device performance. The study aims to demonstrate that the new device's mechanical properties meet these established engineering benchmarks and are comparable to predicate devices.

8. The Sample Size for the Training Set

• Not applicable. This is mechanical testing of a physical medical device, not a machine learning model. There is no "training set" in this context.

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

• Not applicable. See point 8.

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