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510(k) Data Aggregation

    K Number
    K041129
    Manufacturer
    Date Cleared
    2004-09-24

    (147 days)

    Product Code
    Regulation Number
    884.5300
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    PERSONAL LUBRICATING GEL

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

    Personal Lubricating Gel is designed to enhance the ease and comfort of intimate activity and is compatible with latex condoms.

    Device Description

    Personal Lubricating Gel is a non-sterile, clear, non-staining, non-greasy, liquid gel used as a personal lubricant. This product is highly lubricous and may be used with or without a latex condom during intimate sexual activity.

    AI/ML Overview

    The provided document is a 510(k) Premarket Notification for a Personal Lubricating Gel. It details the device's substantial equivalence to a predicate device, its intended use, and non-clinical performance data. However, the document does NOT contain information about specific acceptance criteria related to a study that proves the device meets those criteria, nor does it describe a study involving a "device" in the context of an AI/ML algorithm or diagnostic tool.

    The document focuses on:

    • Substantial Equivalence: Comparing the new lubricating gel to an existing, legally marketed predicate device (K-Y Ultra Gel Personal Lubricant) based on features, chemical properties, ingredients, and perceptual qualities.
    • Non-Clinical Studies: Stability and preservative effectiveness of the new lubricating gel.
    • Intended Use: "designed to enhance the ease and comfort of intimate activity and is compatible with latex condoms."

    Therefore, I cannot provide the requested table and information as it pertains to AI/ML or diagnostic device studies. The document describes a medical device (personal lubricant) and its regulatory submission, which does not typically involve the types of acceptance criteria, test sets, ground truth establishment, or multi-reader studies you've asked about for AI/ML performance.

    Specifically, the information requested below is not present in the provided text:

    1. A table of acceptance criteria and the reported device performance.
    2. Sample sized used for the test set and the data provenance.
    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts.
    4. Adjudication method for the test set.
    5. If a multi-reader, multi-case (MRMC) comparative effectiveness study was done, and its effect size.
    6. If a standalone (algorithm only) performance study was done.
    7. The type of ground truth used.
    8. The sample size for the training set.
    9. How the ground truth for the training set was established.
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