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The Swedge™ Pedicle Screw Fixation System is intended to provide immobilization 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 thoracic, lumbar, and sacral spine: degenerative spondylolisthesis with objective evidence of neurological impairment, fracture, dislocation, deformities or curvatures (i.e. scoliosis, and/or lordosis), spinal tumor, pseudoarthrosis and failed previous fusion.

The Swedge™ Pedicle Screw Fixation System is also intended for non-cervical pedicle screw fixation for the following indications: severe spondylolisthesis (grades 3 and 4) of the LS-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: DDD (degenerative disc disease); trauma (i.e. fracture or dislocation); spinal stenosis; deformities (i.e. scoliosis, kyphosis, and/or lordosis), tumor; pseudoarthrosis; and failed previous fusion.

The Swedge™ Pedicle Screw Fixation System is an implant device made from a titanium alloy (Ti-6Al-4V-ELI) and Cobalt Chrome. The subject device is to be implanted from the posterior approach. The screws are available as either solid or cannulated in diameters from 4.5mm and in lengths from 25mm – 120 mm. Titanium Alloy and Cobalt Chrome rods are available in 4.75mm – 6.0mm diameters either straight or pre-curved in lengths from 25-600 mm. Transition rods are also included with a tapered diameter from 4.75mm -5.5mm and lengths of 60mm - 600mm. The system also includes locking set screws, cross-links connectors, standard, reduction and Long polyaxial tulip heads along with the associated instrumentation to complete the procedure and implant construct.

Based on the provided text, the document is a 510(k) Premarket Notification for a medical device called the "Swedge™ Pedicle Screw Fixation System." This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving a new device's performance through extensive clinical studies with specified acceptance criteria and ground truth establishment in the way an AI/ML device would.

Therefore, many of the requested details, particularly those related to a study proving device performance against acceptance criteria for an AI/ML system (like sample size for test sets, expert adjudication, MRMC studies, standalone algorithm performance, and ground truth establishment for training data), are not applicable to this 510(k) submission for a mechanical pedicle screw fixation system.

The "Performance Testing" section in the document describes mechanical testing, which is standard for orthopedic implants, not software or AI.

Here's a breakdown of what can be extracted or inferred from the provided text, and what is explicitly not present or applicable for this type of device:

Acceptance Criteria and Device Performance (for a Mechanical Device)

For a mechanical device like a pedicle screw system, "acceptance criteria" revolve around established industry standards for mechanical performance (e.g., strength, fatigue, static/dynamic resistance). "Device performance" is the result of these mechanical tests.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document states "Testing shows that the subject Swedge™ Pedicle Screw Fixation System performs equivalent to the predicate Swedge™ Pedicle Screw Fixation System (K170045)." It does not provide specific numerical outcomes of these mechanical tests, but rather a qualitative statement of equivalence to the predicate as the performance conclusion.

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

• Sample Size: Not specified for the mechanical tests. For mechanical testing of medical implants, sample sizes are typically determined by relevant ISO/ASTM standards (e.g., typically n=5 or n=6 for static tests, and larger for fatigue tests, varying by standard and test type). However, the exact number tested is not provided in this public summary.
• Data Provenance: Not applicable in the context of "data" as clinical or imaging data. It refers to in vitro mechanical testing performed according to international standards (ASTM F1717). The location of the testing laboratory is not specified.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not Applicable. This is a mechanical orthopedic implant, not an AI/ML diagnostic or prognostic device that requires human expert review to establish "ground truth" for clinical data.

4. Adjudication Method for the Test Set

Not Applicable. No human interpretation or adjudication of clinical data is involved for this type of mechanical testing.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Not Applicable. MRMC studies are used to evaluate the diagnostic accuracy of AI-assisted systems compared to human readers alone. This is a mechanical device, not an AI system. Therefore, there is no effect size for human readers improving with AI assistance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Not Applicable. This is a mechanical device, not an algorithm.

7. Type of Ground Truth Used

The "ground truth" for this device is the performance against mechanical testing standards (ASTM F1717) and comparison to the established performance of the legally marketed predicate device. It is not clinical "ground truth" derived from expert consensus, pathology, or outcomes data.

8. Sample Size for the Training Set

Not Applicable. As a mechanical device, there is no "training set" in the context of machine learning. The design and manufacturing process are informed by engineering principles, material science, and prior predicate device designs for which performance is already established.

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

Not Applicable. There is no "ground truth" for a training set as this is not an AI/ML device. The design and manufacturing are based on engineering specifications and established material properties and biomechanical principles, validated by mechanical testing against industry standards and comparison to a predicate device.

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