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The Window Cervical Plate System is intended for anterior interbody screw fixation of the cervical spine. The system is indicated for use in the temporary stabilization of the anterior spine during the development of cervical spinal fusions in patients with degenerative disc disease (as defined by neck pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies), trauma (including fractures), tumors, deformity (defined as kyphosis, lordosis, or scoliosis), pseudoarthrosis, and /or failed previous fusions.

The Window Cervical Plate System is a set of implants designed to be implanted via an anterior approach to the cervical spine. The system includes various plates and screws manufactured from Titanium. The material used in the manufacture of the components in the Window Cervical System meets ASTM F136 for Ti 6A1-4VELI.

This is a 510(k) premarket notification for a medical device, not a study evaluating an AI algorithm. Therefore, the requested information about acceptance criteria, device performance, sample sizes, ground truth, expert qualifications, adjudication methods, and comparative effectiveness studies is mostly not applicable in the context of an AI/ML device.

However, I can extract the relevant information within the context of a medical device submission:

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

The submission does not present specific acceptance criteria in the form of performance metrics for the "Window Cervical Dynamic Plate System." Instead, the key "acceptance criteria" here are demonstrating substantial equivalence to a legally marketed predicate device.

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

This submission describes a medical device (surgical implant), not an AI algorithm evaluated on a 'test set' of data in the typical sense. Therefore, concepts like sample size for a test set, data provenance (country, retrospective/prospective), and ground truth are not directly applicable in the way they would be for an AI/ML development.

The "testing" mentioned refers to mechanical and material testing (e.g., ASTM F1717) of the physical device or its components to ensure it meets performance standards and can be considered equivalent to the predicate. The "sample size" for such tests would refer to the number of devices or components tested, not a dataset of patient information. This information is mentioned as being in "Appendix 1" but not detailed in the provided text.

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 is not applicable to this medical device submission. Ground truth, in the context of AI, refers to expert-labeled data. For a physical medical implant, the "truth" is established by engineering standards, material properties, and clinical outcomes over time (post-market surveillance), not by expert labeling of a dataset.

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

Not applicable. Adjudication methods are used to resolve disagreements among human reviewers labeling data for AI/ML training or testing. This process is not part of a 510(k) submission for 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. MRMC studies are used to evaluate diagnostic imaging devices or AI tools that assist human readers. This device is a surgical implant; it does not involve human "readers" or AI assistance in interpretation.

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

Not applicable. This device is a physical surgical implant, not an algorithm.

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

Not applicable. The "truth" for this device revolves around its physical properties, biocompatibility, and mechanical performance, established through engineering tests and adherence to standards (like ASTM F136, ASTM F1717). Its clinical safety and effectiveness are inferred from its substantial equivalence to a predicate device with a history of safe use and similar design/materials.

8. The sample size for the training set

Not applicable. Training sets are used for AI/ML algorithms. This is a physical medical device.

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

Not applicable.

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