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510(k) Data Aggregation

    K Number
    K103755
    Date Cleared
    2011-01-21

    (29 days)

    Product Code
    Regulation Number
    888.3330
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    TAPERLOC COMPLETE, SIZE 5 AND 6

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

    The Taperloc Complete hip stems are intended for uncemented biological fixation. This is the same intended use as the predicate Taperloc® stems cleared in K101086.

    Indications For Use:

    1. Non-inflammatory degenerative joint disease including osteoarthritis and avascular necrosis.
    2. Rheumatoid arthritis
    3. Correction of functional deformity
    4. Treatment of non-union, femoral neck fracture, and trochanteric fractures of the proximal femur with head involvement, unmanageable by other techniques.
    5. Revision procedures where other treatment or devices have failed.

    Porous coated components are intended for uncemented biological fixation.

    Device Description

    The Taperloc® Complete hip stems are a line extension of the legally marketed Taperloc® Complete stems cleared in K101086.

    AI/ML Overview

    Here's an analysis of the provided text regarding the acceptance criteria and supporting studies for the Taperloc® Complete, Sizes 5mm and 6mm:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criterion (Test)Device Performance
    Distal Pot Fatigue Testing (ASTM F-1612-95)All components passed.
    Proximal Pot Fatigue Testing (ASTM F-2068-03)All components passed.

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

    The document does not explicitly state the specific sample size (i.e., number of stems) used for the non-clinical fatigue tests. It only mentions "All components passed."

    The data provenance is from non-clinical testing conducted in accordance with ASTM standards. This implies laboratory testing rather than human subject data. Therefore, questions of country of origin and retrospective/prospective do not apply in the typical sense for clinical studies.

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

    This information is not applicable as the studies were non-clinical fatigue tests based on established ASTM standards, not clinical studies requiring expert interpretation or ground truth establishment in a medical context.

    4. Adjudication Method for the Test Set

    This information is not applicable as the studies were non-clinical fatigue tests. Adjudication methods are typically relevant for clinical trials where multiple evaluators assess outcomes and discrepancies need to be resolved.

    5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

    This information is not applicable. The submission describes a medical device (hip stems) and its non-clinical performance, not an AI-assisted diagnostic or interpretative tool.

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

    This information is not applicable. The device is a physical implant, not an algorithm.

    7. The Type of Ground Truth Used

    The "ground truth" for the non-clinical tests was adherence to the performance requirements specified in the respective ASTM standards:

    • ASTM F-1612-95, Cyclic Fatigue Testing of Metallic Stemmed Hip Arthroplasty Femoral Components with Torsion: The standard itself defines the pass/fail criteria for the fatigue test.
    • ASTM F-2068-03 Standard Specification for Femoral Prostheses - Metallic Implants: This standard specifies the requirements for femoral prostheses, including fatigue testing, and defines what constitutes a "pass."

    8. The Sample Size for the Training Set

    This information is not applicable. The device is a physical implant, and the testing described is non-clinical performance evaluation, not machine learning model training.

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

    This information is not applicable for the same reasons as above.

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