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The G Surgical Marksman MIS System is intended to provide immobilization of the posterior noncervical spine (T1-S2/llium) in skeletally mature patients as an adjunct to fusion in the following acute and chronic instabilities or deformities: degenerative disc disease (defined as back pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies), spondylolisthesis, trauma (i.e., fracture or dislocation), spinal stenosis, curvatures (i.e., scoliosis, kyphosis and/or lordosis), tumor, pseudarthrosis, and/or failed previous fusion. The G Surgical Marksman MIS System can be used in an open approach or a percutaneous approach with MIS instrumentation.

The G Surgical Marksman MIS System consists of longitudinal members (rods), anchors (screws) and fasteners in a variety of sizes to accommodate differing anatomic requirements. The devices are sold nonsterile.

The provided text describes a 510(k) premarket notification for the G Surgical Marksman MIS System. It details the device's indications for use, materials, and references mechanical testing. However, it does not provide specific acceptance criteria or the full study details in the format requested for an AI/software device.

The G Surgical Marksman MIS System is a medical device (a thoracolumbosacral pedicle screw system), not an AI/software device. The performance data mentioned refers to mechanical testing (ASTM F1717) to demonstrate structural integrity and durability, which is standard for orthopedic implants. It does not involve AI performance metrics like accuracy, sensitivity, or specificity, nor does it involve human readers or ground truth established by experts in the context of diagnostic or interpretive tasks.

Therefore, most of the requested information regarding AI/software device performance, such as sample sizes for test/training sets, data provenance, number/qualifications of experts, adjudication methods, MRMC studies, standalone performance, and ground truth types for AI, is not applicable to this submission.

Here is the information that can be extracted or derived from the provided text, acknowledging the nature of the device:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample size: Not explicitly stated, as this refers to mechanical test constructs, not patient data or images for AI. The text mentions "worst-case G Surgical Marksman MIS System constructs."
• Data provenance: Not applicable in the context of patient data for AI. The "data" refers to the results of mechanical laboratory tests.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not applicable. Ground truth in this context would refer to material science and mechanical engineering standards, not expert medical opinion for diagnostic accuracy.

4. Adjudication method for the test set:

• Not applicable. Adjudication is relevant for expert disagreement in AI ground truth establishment. Mechanical testing follows standardized protocols.

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 is not an AI-assisted diagnostic device.

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

• Not applicable. This is not an AI algorithm.

7. The type of ground truth used:

• Ground truth: Adherence to ASTM F1717 standards for mechanical safety and performance, as assessed through laboratory testing.

8. The sample size for the training set:

• Not applicable. This is not an AI device that requires a training set.

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

• Not applicable.

In summary: The provided document is a 510(k) clearance letter for a physical medical device (spinal implant components), not for an AI/software device. The performance data refers to mechanical testing for physical properties, not diagnostic accuracy or human-AI interaction. Therefore, many of the requested details are not relevant to this specific submission.

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