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510(k) Data Aggregation
(64 days)
K2M, PYRENEES CERVICAL PLATE SYSTEM
The K2M Cervical Plate Systems are indicated for use in anterior screw fixation to the cervical spine (C2-C7) for the following indications: degenerative disc disease (DDD) (defined as neck pain of discogenic origin with degeneration of the disc confirmed by history and radiographic studies), spondylolisthesis, trauma (including fractures), spinal stenosis and tumors (primary and metastatic), failed previous fusions (pseudarthrosis) and deformity (defined as scoliosis, kyphosis or lordosis).
The K2M Cervical Plate System is a spinal fixation system which consists of cervical screws and plates. All of the components are available in a variety of sizes to match more closely the patient's anatomy. Materials: The devices are manufactured from titanium alloy and nitinol, per ASTM and ISO standards. Function: The system functions as an adjunct to provide immobilization and stabilization of cervical segments of the spine.
The provided text describes a 510(k) premarket notification for a medical device, the K2M Cervical Plate System, Modifications. This document focuses on demonstrating substantial equivalence to predicate devices, primarily through mechanical testing and comparison of design, materials, and intended use. It does not contain information related to software performance, AI algorithms, clinical study data, or human reader performance. Therefore, most of the requested information cannot be extracted from this document.
However, I can extract information relevant to the device's performance based on the provided text, specifically related to its physical properties and mechanical testing.
Here's a breakdown of the available information:
1. A table of acceptance criteria and the reported device performance
Acceptance Criteria | Reported Device Performance |
---|---|
Performance equal to or better than predicate devices in static torsion and dynamic compression testing per ASTM F1717. | The K2M implants performed equally to, or better than, these systems in static torsion and dynamic compression testing per ASTM F1717. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample Size: Not specified in the provided text. The testing refers to "K2M implants" and "these systems" (predicate systems), but the number of devices tested is not mentioned.
- Data Provenance: Not specified. The testing was conducted by or for K2M, Inc. (based in Leesburg, VA, USA), but the location where the tests were performed is not mentioned. It is a engineering/mechanical test, not patient data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Not applicable. This device is a mechanical implant, and the "ground truth" for its performance is established through standardized mechanical testing (ASTM F1717), not expert interpretation of clinical data.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. This is a mechanical test, not an assessment requiring adjudication by human experts.
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 document describes a medical device (spinal plate system), not an AI or software-based diagnostic tool that would involve human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This device is a mechanical implant, not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The "ground truth" for this device's performance is established by objective mechanical measurements against a recognized international standard (ASTM F1717) for static torsion and dynamic compression in comparison to predicate devices.
8. The sample size for the training set
Not applicable. This is a mechanical device, not a machine learning model. There is no concept of a "training set" in this context.
9. How the ground truth for the training set was established
Not applicable (as above).
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