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510(k) Data Aggregation

    K Number
    K051975
    Manufacturer
    Date Cleared
    2005-09-06

    (47 days)

    Product Code
    Regulation Number
    888.3150
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    POROUS COATED DISCOVERY ELBOW

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

    The Porous Coated Discovery™ Elbow is intended for cemented use in patients with the following conditions:

    1. Non-Inflammatory degenerative joint disease including osteoarthritis, and avascular necrosis.
    2. Rheumatoid arthritis.
    3. Revision where other devices or treatments have failed.
    4. Correction of severe functional deformity.
    5. Treatment of acute or chronic fractures with humeral epicondyle involvement, which are unmanageable using other treatments.
    Device Description

    The Porous Coated Discovery™ Elbow is a total elbow prosthesis comprised of an unfidrar components of components. The humeral components. The humeral through the unfar anticulation into the une and ulnar components are porous coated to provide enhanced fixation.

    AI/ML Overview

    The provided text is a 510(k) summary for the "Porous Coated Discovery™ Elbow." It describes the device, its intended use, and states that substantial equivalence is claimed to a predicate device based on non-clinical mechanical testing. Crucially, it explicitly states "Clinical Testing: None provided as a basis of substantial equivalence."

    Therefore, a detailed breakdown of acceptance criteria, study design, and performance metrics as typically derived from clinical studies cannot be provided because no clinical studies were submitted.

    Here's a summary of the requested information based on the provided document:


    1. Table of Acceptance Criteria and Reported Device Performance:

    • Acceptance Criteria: Not explicitly defined in terms of specific performance metrics within the provided document. The basis for substantial equivalence is "Non-Clinical Testing: Mechanical testing was provided to demonstrate that devices ability to perform." The acceptance criteria would likely be conformity to established mechanical testing standards for this type of prosthetic, demonstrating comparable safety and effectiveness to the predicate device.
    • Reported Device Performance: The document states that the mechanical testing demonstrated the device's "ability to perform." No specific performance metrics (e.g., strength, durability, wear characteristics) or their numerical results are reported in this summary.

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

    • Sample Size for Test Set: Not applicable for clinical data, as no clinical testing was performed for substantial equivalence. For non-clinical mechanical testing, the sample size is not specified in this summary.
    • Data Provenance: Not applicable for clinical data. For non-clinical mechanical testing, the provenance of the test articles would be from the manufacturer (Biomet Manufacturing Corp.).

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

    • Not applicable as no clinical testing was performed and therefore no ground truth established by experts in a clinical context.

    4. Adjudication Method for the Test Set:

    • Not applicable as no clinical testing was performed.

    5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

    • No MRMC comparative effectiveness study was done, as explicitly stated: "Clinical Testing: None provided as a basis of substantial equivalence."

    6. Standalone Performance Study (Algorithm Only):

    • Not applicable. This is a physical medical device (elbow prosthesis), not an AI algorithm.

    7. Type of Ground Truth Used:

    • For the non-clinical mechanical testing, the "ground truth" would be the engineering specifications and established performance standards for elbow prostheses. The document does not specify if these standards were internal, industry-wide, or regulatory.

    8. Sample Size for the Training Set:

    • Not applicable for physical device testing in this context. Training sets are relevant for machine learning algorithms.

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

    • Not applicable.
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