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510(k) Data Aggregation
(319 days)
ANATOMICA POSTERIOR CERVICAL FIXATION SYSTEM
The Anatomica Posterior Cervical Fixation System is intended to promote fusion of the cervical spine and occipito-cervico-thoracic junction (Occiput - T3). The system is intended for posterior, cervical, non-pedicle fixation, or for posterior, noncervical pedicle fixation for the following indications:
- . degenerative disc disease(DDD) (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.
Occipital bone screws are limited to occipital fixation only. Pedicle bone screws are limited to placement in the upper thoracic spine (T1, T3) when treating thoracic conditions only. Pedicle screws are not intended to be placed in the cervical spine. Hooks and wires (not pedicle screws) are used to achieve cervical fusion for the occipital/cervical loop.
The Anatomica Cervical System is a titanium spinal fixation system for occipito-cervico-thoracic (OCT) fixation that contains screws, hooks, and wires that are assembled to rods using one of several types of connectors (expansion loop, compression loop, ME connector, lateral connector, cable lock connector). The occiput screws are for use in the occiput. The wires are for use in the cervical spine. The screws are for use in the thoracic spine (T1-T3). The hooks are for use in the cervical spine.
The provided text describes a spinal fixation system, not an AI/ML powered device, therefore the typical acceptance criteria and study designs relevant to AI/ML powered devices (such as clinical performance metrics, ground truth establishment, reader studies, etc.) are not applicable.
Instead, the document focuses on the mechanical and material performance of the physical implant device.
Here's an analysis based on the information provided, reinterpreting your requested categories for a non-AI medical device:
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria Category | Reported Device Performance (Study) |
---|---|
Mechanical Performance | |
Static Bend | Tested in accordance with ASTM F1717-01 |
Fatigue Bend - Fatigue Life | Tested in accordance with ASTM F1717-01 |
Fatigue Bend - Incremental Load Block | Tested in accordance with ASTM F1717-01 |
Torsion | Tested in accordance with ASTM F1717-01 |
Note: The document states that the performance testing was "in accordance with ASTM F1717-01, Standard test methods for spinal implant constructs in a vertebrectomy model." While it doesn't explicitly list numerical acceptance criteria values (e.g., "must withstand X N-m for static bend"), complying with a recognized ASTM standard implies that the device met the performance requirements/benchmarks specified within that standard for the tested parameters. The "Reported Device Performance" is the act of having performed these tests and presumably achieving results comparable to the predicate device and within the ASTM standard's expectations for safety and effectiveness. The document also states "mechanical testing to have similar performance characteristics" to the predicate device.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: The document mentions "Articles provided from regular production lots." It does not specify a numerical sample size (e.g., number of screws, rods, or construct assemblies tested). For mechanical testing of medical devices, sample sizes are typically determined by statistical methods to ensure sufficient confidence in the results, but the specific numbers are not disclosed here.
- Data Provenance: Not applicable in the context of clinical data provenance (e.g., country of origin, retrospective/prospective). This refers to physical samples from "regular production lots" of the manufactured device.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts
Not applicable. "Ground truth" in this context would implicitly be the physical properties and mechanical behavior of the materials and device constructs as measured by standardized testing equipment, against a benchmark defined by the ASTM standard. There isn't a human expert consensus involved in establishing this "ground truth."
4. Adjudication Method for the Test Set
Not applicable. This concept pertains to resolving discrepancies in human expert interpretations, which is not relevant for mechanical device testing.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No. This type of study is for evaluating human interpretation with and without assistance from an AI. The device described is a physical spinal implant system undergoing mechanical performance testing, not a diagnostic AI tool.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Not applicable. This device is not an algorithm.
7. The Type of Ground Truth Used
- Mechanical Performance Standards: The "ground truth" for the device's performance is established by the specified ASTM F1717-01 standard. This standard dictates the methods for testing and implicitly sets performance expectations for spinal implant constructs in a vertebrectomy model. The performance of the subject device is then compared against these established standards and its predicate devices.
- Predicate Device Performance: The basis for substantial equivalence also relies on the subject device showing "similar performance characteristics" to its legally marketed predicate devices, the Altius OCT System. Therefore, the established performance profile of the predicate device acts as another form of "ground truth" or benchmark.
8. The Sample Size for the Training Set
Not applicable. This device does not involve a "training set" in the machine learning sense. The "training" for a physical device is its design, engineering, and manufacturing process, optimized through experience and engineering principles, but not through data-driven machine learning.
9. How the Ground Truth for the Training Set Was Established
Not applicable for the same reasons as #8.
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