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510(k) Data Aggregation
(28 days)
The M.U.S.T. Pedicle Screw System is intended for posterior non-cervical pedicle fixation (T1-S2/ilium) or anterolateral fixation (T8-L5). These devices are indicated as an adjunct to fusion for all of the following indications: 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; pseudoarthrosis and failed previous fusion in skeletally mature patients.
The M.U.S.T. Extension is intended to be used as part of the M.U.S.T. pedicle screw system (K121115, K132878) for the stabilization and the fusion of the lumbar and thoracic spine. The M.U.S.T. pedicle screw system includes cannulated or non cannulated poly-axial pedicle screws (K121115, K132878), cannulated or non cannulated mono-axial pedicle screws (K132878), set screws (K121115), straight and pre-bent rods (K121115), and cross connectors (K132878).
The M.U.S.T. Extension introduces a new design of the pedicle screws and rods, featuring additional mechanical interfaces for dedicated instruments designed for percutaneous surgery. On the pedicle screw head (Tulip), there are four additional small grooves on the upper rim to enhance the attachment to the Percutaneous Tower. Additionally, the tip of the screw is more tapered to enhance the insertion into the bone, in case the bone tap is not used (cannulated screws only). The Rods are provided with a hexagonal interface on one extremity and a bulleted nose on the opposite, to allow insertion by means of a dedicated handle through the tissue. The Rods have new intermediate lengths and the bent rods have an increased curvature.
The provided document is a 510(k) premarket notification for a medical device called the "M.U.S.T. Pedicle Screw System." This type of document is used to demonstrate that a device is substantially equivalent to a legally marketed predicate device, rather than proving its safety and effectiveness through a clinical study with acceptance criteria in the same way a PMA (Premarket Approval) would.
Therefore, the requested information for acceptance criteria and a study proving the device meets these criteria in the context of an AI medical device is not directly applicable here. This document describes a traditional spinal implant, and the performance testing is focused on mechanical characteristics rather than algorithmic performance.
However, I can extract the information relevant to how the device's "performance" (mechanical integrity and equivalence) was assessed based on the provided text:
1. A table of acceptance criteria and the reported device performance:
The document states that "design verification was conducted to written protocols with pre-defined acceptance criteria. The protocols and pre-defined acceptance criteria were based on the standards, FDA guidance, and comparison to the predicate device system." It also mentions that "The M.U.S.T. Extension was compared to the worst case predicate device and it was determined that the M.U.S.T. Extension is not worst case." This implies that the acceptance criteria for each test were met if the M.U.S.T. Extension performed at least as well as or better than the worst-case predicate.
However, the specific quantitative acceptance criteria or the reported device performance values are not explicitly listed in the provided text. The document refers to the following tests (from ASTM F 1717) without providing the actual results:
| Test Type | Standard | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|---|
| Static compression/bending yield strength | ASTM F 1717 | Performance at least equivalent to worst-case predicate. | Not explicitly reported. |
| Fatigue compression/bending strength | ASTM F 1717 | Performance at least equivalent to worst-case predicate. | Not explicitly reported. |
| Static compression/bending stiffness | ASTM F 1717 | Performance at least equivalent to worst-case predicate. | Not explicitly reported. |
| Static torsion yield strength | ASTM F 1717 | Performance at least equivalent to worst-case predicate. | Not explicitly reported. |
| Static torsion stiffness | ASTM F 1717 | Performance at least equivalent to worst-case predicate. | Not explicitly reported. |
2. Sample size used for the test set and the data provenance:
- Sample Size: The document does not specify the sample size for the mechanical performance testing. This information would typically be detailed in the test protocols themselves, which are not included in this summary.
- Data Provenance: The testing was "conducted to written protocols," implying laboratory-based mechanical testing. The origin of the components would be Medacta International SA (Switzerland) and Medacta USA. The nature of the testing (mechanical stress, fatigue, etc.) suggests it's a prospective lab study rather than human clinical data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This question is not applicable to this type of device. There is no concept of "ground truth" established by experts in the context of mechanical performance testing of a pedicle screw system. The evaluation is based on objective measurements against engineering standards.
4. Adjudication method for the test set:
Not applicable. There's no "adjudication" required for mechanical testing results; they are empirical measurements.
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 a spinal implant, not an AI-powered diagnostic or assistive tool. Therefore, no MRMC study or AI-related effectiveness assessment was performed or is relevant.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
Not applicable. This is not an algorithmic device.
7. The type of ground truth used:
For mechanical testing, the "ground truth" is typically defined by engineering standards (e.g., ASTM F 1717) and the measured physical properties of the materials and devices.
8. The sample size for the training set:
Not applicable. This is not a machine learning or AI device that would have a "training set."
9. How the ground truth for the training set was established:
Not applicable.
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