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The CarboClear Pedicle Screw System is intended to restore the integrity of the spinal column even in the absence of fusion for a limited time period in patients with advanced-stage tumors involving the thoracic and lumbar spine in whom life expectancy is of insufficient duration to permit achievement of fusion.

The CarboClear Pedicle Screw System is composed of implants in various dimensions, used to build a spinal construct; and of a set of instruments, intended to assist in the insertion and placement of the implants.

The CarboClear implants include pedicle screws, rods, locking elements and transverse connectors. The implants are made of carbon fiber-reinforced polyetheretherketone (CFR-PEEK). The threaded portion of the pedicle screws is encased within a thin titanium shell, and includes a small tantalum marker.

The implants are supplied sterile, and are intended for single use.

This document is a 510(k) premarket notification for a medical device called the CarboClear® Pedicle Screw System. It is primarily concerned with establishing substantial equivalence to a predicate device, rather than proving performance against specific acceptance criteria in a clinical study. Therefore, much of the requested information about clinical studies, expert-established ground truth, and specific performance metrics for an AI device is not applicable or available within this document.

Here's an attempt to answer the questions based on the provided text, highlighting where information is not present:

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

This document does not provide a table of acceptance criteria in the context of device performance in a clinical setting (e.g., accuracy, sensitivity, specificity for an AI device). Instead, it focuses on demonstrating substantial equivalence to a predicate device through mechanical and material testing. The "acceptance criteria" in this context are implicitly met if the test results are "comparable to those of the predicate device."

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

Not applicable. This submission relies on mechanical and material testing, not a test set of patient 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)

Not applicable. This submission does not involve clinical data or ground truth established by medical experts for a test set.

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

Not applicable. This submission does not involve adjudication of a test set.

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 document describes a medical implant, not an AI device, and therefore no MRMC study was conducted or reported.

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

Not applicable. This document describes a medical implant, not an AI device.

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

Not applicable. The "ground truth" in this context relates to the mechanical and material properties of the device, which are evaluated against established engineering standards (ASTM) and compared to the predicate device, rather than clinical ground truth like pathology or outcomes data.

8. The sample size for the training set

Not applicable. This submission deals with a physical medical device and its mechanical testing, not a machine learning model with a training set.

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

Not applicable. There is no training set for a machine learning model.

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