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510(k) Data Aggregation

    K Number
    K011799
    Device Name
    EVERSTICKNET
    Manufacturer
    Date Cleared
    2001-11-05

    (150 days)

    Product Code
    Regulation Number
    872.3760
    Panel
    Dental
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    As reinforcement in manufacturing and/or repairing full or partial dentures as well as overdentures and orthodontic appliances. As reinforcement for temporary and/or permanent plastic/composite partial and full crowns and bridges. As reinforcement for customized splints used to immobilize teeth which may be required for post-trauma, post-operative, or orthodontic therapy.

    Device Description

    everStickNet™ is a semi-manufactured product made of glass fibers and polymer/resin matrix for reinforcing dental acrylic polymers. everStickNet™ is made of a thin fiberglass fabric, which increase the strength and stiffness of the final product in all directions.

    AI/ML Overview

    The provided text is a 510(k) summary for the everStickNet™ device, which is a glass fiber reinforcement material. The document focuses on establishing substantial equivalence to a predicate device (fibreNet™) rather than presenting a study with specific acceptance criteria and performance metrics for the everStickNet™ itself.

    Therefore, many of the requested details, such as a table of acceptance criteria, sample sizes for test sets, expert ground truth establishment, adjudication methods, MRMC studies, standalone performance, and training set details, are not available in this document.

    The core information presented is a comparison to a predicate device and a statement regarding the safety and effectiveness based on that comparison.

    Here's a breakdown of what can be extracted based on the provided text:

    1. A table of acceptance criteria and the reported device performance:

    This information is not provided in the document. The filing focuses on substantial equivalence to a predicate device, stating that "Test results indicate that there are no hazards presented with the use of everStickNet™ as compared with the predicate device." It does not define specific performance acceptance criteria for the everStickNet™ in terms of quantifiable metrics.

    2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

    This information is not provided. The document mentions "Testing which has been performed on everStickNet™" but does not detail the nature of this testing, including sample size or data provenance.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

    This information is not provided. The assessment appears to be based on technical characteristics and comparison to a predicate device, not on diagnostic or interpretative tasks requiring expert ground truth.

    4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

    This information is not provided.

    5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

    This information is not provided. The device is a material for dental reinforcement, not an AI-assisted diagnostic 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 material, not an algorithm.

    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

    This information is not explicitly stated. The "test results" mentioned likely refer to material property testing (e.g., strength, stiffness) rather than a ground truth for diagnostic accuracy or outcomes. The basis for safety and effectiveness is stated as being "as safe and effective as the predicate device" based on "different technological characteristics" that "does not raise new questions of safety and effectiveness."

    8. The sample size for the training set:

    This information is not applicable/not provided. The device is a material, not a machine learning model that requires a training set.

    9. How the ground truth for the training set was established:

    This information is not applicable/not provided.

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