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510(k) Data Aggregation

    K Number
    K012279
    Device Name
    PATTON TRIPOL
    Manufacturer
    PATTON MEDICAL CORP.
    Date Cleared
    2002-04-30

    (285 days)

    Product Code
    GEI
    Regulation Number
    878.4400
    Type
    Traditional
    Panel
    General & Plastic Surgery
    Reference & Predicate Devices
    K993055,K971565
    Why did this record match?
    Device Name :

    PATTON TRIPOL

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

    The Patton Tripol™ is intended for use in open and laparoscopic surgeries where grasping, coagulating, and transecting of tissue is indicated.

    Device Description

    The Patton Tripol™ is a forceps that grasps, coagulates, and transects tissue, utilizing electrical current. The device is compatible with available bipolar generators.

    AI/ML Overview

    This document is a 510(k) summary for the Patton Tripol™ Bipolar Forceps. It does not contain information about the acceptance criteria or a study proving the device meets acceptance criteria in the context of an AI/ML medical device.

    The document describes a medical device (bipolar forceps) that grasps, coagulates, and transects tissue using electrical current. The submission is focused on demonstrating substantial equivalence to existing predicate devices (Dexide Bipolar Forceps II+ Device and Bicoag Coagulating Forceps).

    Therefore, I cannot provide the requested information from the provided text, as it pertains to a traditional medical device submission and not to an AI/ML device study.

    Here's why each point cannot be addressed:

    1. A table of acceptance criteria and the reported device performance: Not present. Substantial equivalence claims for traditional devices often rely on material properties, design comparisons, and functional testing, not statistical performance metrics like sensitivity/specificity against a ground truth.
    2. Sample size used for the test set and the data provenance: Not applicable. There's no "test set" in the context of an AI/ML model.
    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI/ML model is not relevant here.
    4. Adjudication method: Not applicable.
    5. If a multi reader multi case (MRMC) comparative effectiveness study was done: Not applicable. This type of study is for evaluating human performance with and without AI assistance.
    6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. There is no algorithm.
    7. The type of ground truth used: Not applicable.
    8. The sample size for the training set: Not applicable. There is no training set for an AI/ML model.
    9. How the ground truth for the training set was established: Not applicable.
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