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The icotec Pedicle System is intended to provide immobilization of the posterior, noncervical spine in skeletally mature patients as an adjunct to 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, and/or lordosis); tumor; pseudoarthrosis; and failed previous fusion.

The icotec Pedicle System is a posterior pedicle screw system manufactured from titanium alloy (Ti6Al4V ELI per ASTM F136) designed for fixation of the non-cervical spine. The icotec Pedicle System is comprised of monoaxial and polyaxial pedicle screws and curved and straight rods. The icotec Pedicle System can be used for single or multiple level fixations.

This document is a 510(k) premarket notification for the icotec Pedicle System, a medical device. It describes the device, its intended use, and its substantial equivalence to previously marketed predicate devices.

Here's an analysis of the provided text in the context of the requested information about acceptance criteria and evidence of meeting them:

Critically, this document is a 510(k) submission for a pedicle screw spinal system, a physical medical device (implant). It is not a document describing an AI/ML medical device submission. Therefore, the concepts of "acceptance criteria" and "study proving the device meets the acceptance criteria" as they apply to software performance (e.g., sensitivity, specificity, AUC) are not directly applicable in the way your prompt expects for an AI/ML device.

This 510(k) demonstrates substantial equivalence primarily through:

• Intended Use, Material, Geometry, Method of Fixation, and Mechanical Properties comparison with predicate devices.
• Mechanical Testing (non-clinical) to show that the device performs equivalently to previously cleared devices under simulated physiological conditions.

Given this, I will interpret your questions in the context of a physical medical device's submission dossier.

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

For a physical device like a pedicle screw system, acceptance criteria are generally related to mechanical performance, material properties, and design specifications. The document states that the device meets these through comparison to predicates and non-clinical testing.

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

For physical device mechanical testing (preclinical testing), "sample size" refers to the number of devices or test specimens used in the mechanical tests (e.g., number of screws tested for bending strength). This information is not provided in this summary. Such details would typically be in the full test reports submitted to the FDA, not in the 510(k) summary.

Data provenance: The mechanical tests were performed by "the company" (icotec AG). The tests are "non-clinical," implying laboratory-based testing rather than patient data.

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

This concept is not directly applicable to a physical device's mechanical or material testing. There isn't "ground truth" derived from expert consensus for device mechanical properties in the way there is for image interpretation in an AI/ML study. The "ground truth" for mechanical testing is established by engineering standards (e.g., ASTM F1717) and the physical measurements themselves.

4. Adjudication method for the test set

Not applicable for mechanical testing of a physical device.

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 510(k) for a physical implant, not an AI/ML diagnostic or assistive device.

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

Not applicable. This is a 510(k) for a physical implant, not an AI/ML algorithm.

7. The type of ground truth used

The "ground truth" for this device's performance is based on engineering standards (ASTM F1717) and the measured mechanical properties of the device components, demonstrating equivalence to the predicate devices. There is no biological/clinical "ground truth" from patients or pathology in this specific context of demonstrating substantial equivalence via preclinical testing.

8. The sample size for the training set

Not applicable. There is no "training set" in the context of a traditional 510(k) for a physical medical device. The device design and manufacturing process are established, and then the product is tested against established standards and predicates.

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

Not applicable. See point 8.

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