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The UNIPLATE Anterior Cervical Plate System is intended for anterior cervical intervertebral body fixation. This system is indicated for patients in which stability is desired following anterior cervical fusion for the indications listed below. The intended levels for treatment range from C2-T1. Indications include symptomatic cervical spondylosis, trauma, fracture, post-traumatic kyphosis or lordosis, tumor, degenerative disc disease (defined as discogenic pain with degeneration of the disc confirmed by history and radiographic studies), spinal stenosis, re-operation for failed fusion, or instability following surgery for the above indications.

The UNIPLATE Anterior Cervical Plate System consists of an assortment of plates and screws. The UNIPLATE Anterior Cervical Plate System also contains Class I manual surgical instruments and cases that are considered exempt from premarket notification. Manufactured from ASTM F 136 implant grade titanium alloy.

The provided text is a 510(k) summary for the UNIPLATE Anterior Cervical Plate System. It describes the device, its intended use, and indicates that performance data were submitted to characterize the device. However, it does not contain detailed information about specific acceptance criteria or the study data proving the device meets those criteria in the way typically expected for an AI/ML device.

This document is for a medical implant (spinal fixation system), not a diagnostic AI/ML device. Therefore, the questions regarding AI/ML performance metrics like sample sizes for test/training sets, ground truth establishment, expert qualifications, adjudication methods, or MRMC studies are not applicable to this submission.

Here's a breakdown of what can be extracted from the provided text, and where it falls short for an AI/ML device context:

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

The document states: "Performance data were submitted to characterize the subject UNIPLATE Anterior Cervical Plate System components addressed in this notification."

However, it does not provide a table of acceptance criteria or reported device performance in the typical sense of accuracy, sensitivity, specificity, etc., that would be applicable to an AI/ML diagnostic or measurement device. For an implantable device like this, "performance data" likely refers to biomechanical testing (e.g., fatigue, static strength, subsidence testing) to ensure it can withstand the forces in the spine and maintain stability. These specific results and their acceptance criteria are not detailed in this summary.

2. Sample sized 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 and not present as this is a medical implant, not a diagnostic AI/ML device. There is no "test set" in the context of image analysis 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 and not present as this is a medical implant, not a diagnostic AI/ML device. Ground truth as typically defined for AI/ML (e.g., expert consensus on images) is not relevant here.

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

This information is not applicable and not present as this is a medical implant, not a diagnostic AI/ML 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

This information is not applicable and not present as this is a medical implant, not a diagnostic AI/ML device. MRMC studies are used for evaluating diagnostic performance of human readers with and without AI assistance.

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

This information is not applicable and not present as this is a medical implant, not an algorithm.

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

This information is not applicable and not present directly in the provided text. For an implant, "ground truth" might pertain to established biomechanical standards or clinical outcomes from prior similar devices (often addressed through substantial equivalence).

8. The sample size for the training set

This information is not applicable and not present as this is a medical implant, not a machine learning algorithm.

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

This information is not applicable and not present as this is a medical implant, not a machine learning algorithm.

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