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The Dymaxeon Spine System is 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 thoracic, lumbar, and sacral/iliac spine (T1 - S1/Ileum): degenerative disc disease (defined as discogenic back pain with degeneration of disc confirmed by history and radiographic studies), degenerative spondylolisthesis with objective evidence of neurological impairment, fracture, dislocation, scoliosis, kyphosis, spinal tumor, and failed previous fusion (pseudarthrosis).

The Dymaxeon Spine System is a posterior pedicle screw system manufactured from titanium alloy (Ti6Al4V ELI per ASTM F136) designed for temporary stabilization of the spine during the development of spinal fusion. The Dymaxeon Spine System is comprised of polyaxial pedicle screws, reduction screws, and rods. The Dymaxeon Spine System can be used for single or multiple level fixations. The pedicle screws are available in various lengths and diameters.

The provided text describes the Dymaxeon Spine System, a posterior pedicle screw system, and its substantial equivalence to predicate devices based on preclinical testing. It does not describe a study involving humans or AI for diagnostic or predictive purposes. Therefore, most of the requested information regarding acceptance criteria, sample sizes, expert involvement, and comparative effectiveness with AI assistance is not applicable to this document.

Here's the information that can be extracted or deduced from the provided text:

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

The document does not explicitly state numerical acceptance criteria in a table format. However, the reported device performance is that it met the requirements of the ASTM F1717 standard. The acceptance criterion is implied to be "substantially equivalent" to predicate devices, as demonstrated by meeting this standard.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

This information is not applicable as the described study is preclinical biomechanical testing, not a human reader study or a study using clinical 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)

This information is not applicable. Ground truth, in the context of this preclinical testing, would relate to the physical properties and performance of the device against established engineering standards, not expert medical opinions on diagnostic accuracy.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

This information is not applicable. Adjudication methods are relevant for studies involving human interpretation or clinical outcomes, not for preclinical mechanical testing.

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

This information is not applicable. The provided text describes a medical device (spinal implant) and its preclinical testing, not an AI-powered diagnostic or assistive tool.

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

This information is not applicable. This document does not describe an algorithm or AI.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The "ground truth" for this preclinical testing was established by standardized biomechanical testing protocols, specifically ASTM F1717. This standard defines methods for testing spinal implant constructs, and meeting its requirements (as compared to predicate devices) serves as the basis for demonstrating substantial equivalence.

8. The sample size for the training set

This information is not applicable. There is no "training set" in the context of preclinical mechanical testing of a spinal implant. This term is relevant for machine learning algorithms.

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

This information is not applicable for the reason stated above.

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