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The DePuy Pulse Anterior Cervical Plate System is indicated for stabilization of the cervical spine from C2 to T1 employing unicortical screw fixation at the anterior face of the vertebral bodies. Specific clinical indications for anterior plating include: instability caused by trauma; instability associated with correction of cervical lordosis and kyphosis deformity; instability associated with pseudoarthrosis as a result of previously failed cervical spine surgery; instability associated with major reconstructive surgery for primary tumors or metastatic malignant tumors of the cervical spine; instability associated with single or multiple level corpectomy in advanced degenerative disc disease, spinal canal stenosis and cervical myelopathy.

The DePuy Pulse Anterior Cervical Plate System is intended for anterior screw fixation of the plate to the cervical spine. The fixation construct consists of a cervical plate that is attached to the vertebral body of the cervical spine with self-tapping bone screws using an anterior approach. The DePuy Pulse Anterior Cervical Plate System consists of an assortment of implantable titanium alloy plates and screws in various sizes.

The DePuy Pulse Anterior Cervical Plate System also contains Class 1 manual surgical instruments and cases that are considered exempt from premarket notification.

The provided text describes the DePuy Pulse Anterior Cervical Plate System, a spinal fixation device, and its non-clinical testing. Here's a breakdown of the acceptance criteria and the study details:

1. Table of Acceptance Criteria and Reported Device Performance:

Note: The specific numerical or qualitative acceptance criteria for each test (e.g., maximum deflection, load before failure, cycles to failure) are not provided in this summary. The summary only states that the criteria were met.

2. Sample Size Used for the Test Set and Data Provenance:

The document states "No clinical tests were performed." Therefore, there is no test set in the context of clinical data, as this device's submission relied on non-clinical mechanical testing and substantial equivalence to predicate devices. The mechanical tests themselves involve physical samples of the device components. The sample sizes for these mechanical tests are not explicitly stated in the provided text. The data provenance is non-clinical mechanical testing, likely conducted in a laboratory setting by the manufacturer or contracted lab.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications:

Not applicable, as no clinical studies were performed, and thus no expert ground truth was established for a test set of clinical data.

4. Adjudication Method for the Test Set:

Not applicable, as no clinical studies were performed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done, as stated by "No clinical tests were performed."

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done:

Not applicable, as this device is a physical medical implant, not a software algorithm.

7. The Type of Ground Truth Used:

For the non-clinical mechanical tests, the "ground truth" would be the engineering specifications and performance standards defined by ASTM F-1717 and internal requirements, which the device's performance was measured against. This is essentially engineering specifications/standards compliance.

8. The Sample Size for the Training Set:

Not applicable, as this device is a physical medical implant and does not involve AI/machine learning algorithms that require a training set.

9. How the Ground Truth for the Training Set was Established:

Not applicable, as this device is a physical medical implant and does not involve AI/machine learning algorithms that require a training set and corresponding ground truth.

In summary, this submission relies entirely on non-clinical mechanical testing to demonstrate substantial equivalence, rather than clinical studies or AI algorithm performance.

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