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The PSS System is intended to provide immobilization and stabilization of spinal segments in skeletally mature patients as an adjunct to fusion in the treatment of the following acute and chronic instabilities or deformities of the thoracic, lumbar, and sacral spine: degenerative spondylolisthesis with objective evidence of neurologic impairment, fracture, dislocation, scoliosis, kyphosis, spinal tumor, and failed previous fusion (pseudoarthrosis).

The PSS System is also intended for non-cervical pedicle screw fixation for the following indications: severe spondylolisthesis (grades 3 and 4 of the L5-S1 vertebra in skeletally mature patients receiving fusion by autogenous bone graft having implants attached to the lumbar and sacral spine (L3 to sacrum) with removal of the implants after the attainment of a solid fusion. It is also intended for the following indications: trauma (i.e. fracture or dislocation); spinal stenosis; curvatures (i.e. scoliosis; and/or lordosis); spinal tumor; pseudoarthrosis; and failed previous fusion.

The PSS System is a top-loading, multiple component, posterior spinal fixation system which consists of pedicle screws, rods, cross links, locking cap screws. All of the components are available in a variety of sizes to match more closely the patient's anatomy. All components are made from medical grade titanium or titanium alloy described by such standards as ASTM F136 or ISO5832-3. The products are supplied clean and "NON-STERILE".

The provided text describes a 510(k) premarket notification for a medical device called the "PSS System," a pedicle screw spinal system. This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study to prove performance against specific acceptance criteria in the manner typically seen for AI/ML devices. Therefore, much of the requested information regarding AI/ML device studies (such as sample sizes for test and training sets, expert qualifications, and MRMC studies) is not applicable to this submission.

Here's a breakdown based on the provided text, highlighting where information is not relevant or available for this type of submission:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not explicitly stated as a "test set" in the context of an AI/ML device. The "test set" here refers to the physical samples of the PSS System tested according to ASTM F1717. The number of physical units tested is not specified in this summary.
• Data Provenance: Not applicable in the context of clinical data. This is a mechanical testing study.

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

• Not Applicable. This is a mechanical device, and "ground truth" as it pertains to expert consensus on clinical data is not relevant. The "ground truth" for mechanical testing is defined by the performance standards outlined in ASTM F1717.

4. Adjudication method for the test set

• Not Applicable. Mechanical testing does not involve adjudication by experts in the same way clinical image interpretation or diagnosis does.

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

• No. This is a mechanical spinal fixation system, not an AI/ML device. MRMC studies are not relevant.

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

• No. This is a mechanical device. Standalone algorithm performance is not relevant.

7. The type of ground truth used

• For mechanical devices, the "ground truth" is typically defined by the performance requirements and testing methodologies specified in recognized standards (e.g., ASTM F1717) and successful performance of a legally marketed predicate device. In this case, the PSS system's performance was compared to a previously cleared predicate device (K071438) using ASTM F1717.

8. The sample size for the training set

• Not Applicable. This is a mechanical device, not an AI/ML algorithm that requires a training set.

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

• Not Applicable.

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