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The TriFix Spinal System is indicated for degenerative disc disease (defined as discogenic back pain with degeneration of the disc confirmed by history and radiographic studies). Levels of fixation are for the thoracic, lumbar, and sacral spine.

The Tri-Fix Spinal System is a pedicle screw system 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 posterior Tri-Fix Spinal System is also indicated for pedicle screw fixation for severe spondylolithesis (grades 3 and 4) at L5-S1, in skeletally mature patients, when autogenous bone graft is used, when affixed to the posterior lumbosacral spine, and intended to be removed after solid fusion is attained. Levels of fixation are from L3-S1.

The posterior Tri-Fix System, when not used with pedicle screws is indicated for hook, wire, and/or sacral screw fixation from T1 to the ilium sacrum. The non-pedicle screw indications are spondylolithesis, degenerative disc disease, (defined as discogenic back pain with degeneration of the disc confirmed by history and radiographic studies), deformities (scoliosis, lordosis and kyphosis), tumor, fracture, and previous failed fusion surgery.

The TrFix Washerless Plate system is intended to be used for posterior lumbar fusion procedures. The system is manufactured from titanium which complies with ASTM F136. The components, which are included as part of the system, include screws, rods, plates, and accessory connection components.

The provided text describes a 510(k) summary for the Tri-Fix Washerless Plate system. This is a medical device, and the submission focuses on demonstrating substantial equivalence to a previously cleared device, not on clinical performance metrics like sensitivity, specificity, or reader studies. Therefore, many of the requested categories (such as experts, adjudication, effect size, standalone performance, ground truth types, and training set details) are not applicable to this type of submission.

Here's an analysis based on the information provided:

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

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

The study was a biomechanical test, not a clinical study involving human patients. The "sample size" refers to the number of constructs tested.

• Test Set Sample Size: Not explicitly stated in terms of a specific number of constructs, but implied as sufficient to conduct "both static and fatigue compression tests."
• Data Provenance: Not applicable in the sense of country of origin for patient data, as this was a biomechanical engineering test. It was a non-clinical, in-vitro study.

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

Not applicable. This was a biomechanical engineering test, not a clinical study requiring expert ground truth for diagnostic accuracy. The "ground truth" was the physical performance of the device under stress, measured against engineering standards.

4. Adjudication method for the test set

Not applicable. This was a biomechanical test with objective physical measurements, not a test requiring human adjudication of results.

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 a surgical implant, not an AI-powered diagnostic tool. No MRMC study was conducted.

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

Not applicable. This device is a physical surgical implant, not an algorithm.

7. The type of ground truth used

The ground truth for this non-clinical study was the biomechanical performance of the device components when tested against established industry standards (ASTM F1717) for static and fatigue compression.

8. The sample size for the training set

Not applicable. As a biomechanical engineering study to demonstrate substantial equivalence, there is no "training set" in the context of machine learning or AI.

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

Not applicable. There was no training set for this biomechanical engineering study.

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