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510(k) Data Aggregation

    K Number
    K200519
    Device Name
    CarboClear® II Pedicle Screw System
    Manufacturer
    CarboFix Orthopedics Ltd.
    Date Cleared
    2020-04-30

    (59 days)

    Product Code
    NKB
    Regulation Number
    888.3070
    Type
    Traditional
    Panel
    Orthopedic (OR)
    Reference & Predicate Devices
    K102369,K123810,K173487,K182377,K130291,K182928,K052398
    Why did this record match?
    Reference Devices :

    K102369, K123810

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The CarboClear® II 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.

    Device Description

    The CarboClear® II 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® II implants include pedicle screws, rods, locking element 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.

    The locking mechanism of the screws is being modified to lower their profile and simplify the surgical technique. In addition, larger diameter and longer screws are being added to the system.

    AI/ML Overview

    This is a 510(k) premarket notification for the CarboClear® II Pedicle Screw System, a medical device, and therefore does not include a study on the performance of an AI/ML algorithm.

    The document describes the device, its intended use, and its substantial equivalence to predicate devices based on performance testing. It does not involve an AI/ML algorithm or associated acceptance criteria and studies.

    Therefore, I cannot extract the requested information regarding acceptance criteria and studies proving the device meets them because the provided text does not discuss an AI/ML device.

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