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A patient examination glove is a disposable device intended for medical purposes that is worn upon the examiner's hands or fingers to prevent contamination between patient and examiner.

A Powder-free Clear Vinyl Patient Examination Gloves is a disposable device intended for medical purposes that is worn upon the examiner's hands or finger to prevent contamination between patient and examiner (21CFR 880.6250). Our Powder-free Clear Vinyl Patient Examination Gloves do not contain any UPS powder according to the results of testing conducted on the gloves. The residual powder testing is conducted per standards of ASTM D-6124-06, which demonstrates that the powder free glove is less than 2mg/pc. Classified by FDA's General and Plastic Surgery Device panel as Class I, 21 CFR 880.6250, Powder-Free Vinyl Patient Examination Glove, 80LYZ, and meets all requirement of ASTM Standard D5250-06.

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size used for the test set and the data provenance:

   • Test Set Sample Sizes:
     • Pinhole Results: 125 gloves.
     • Residual Powder: Not explicitly stated but testing performed "per standards of ASTM D-6124-06" which would imply a specific sampling plan.
     • Tensile Strength & Elongation (Before & After Aging): Not explicitly stated, but implies a sample of gloves tested according to ASTM-D-5250-06.
     • Biocompatibility (Primary Skin Irritation & Dermal Sensitization): A study was conducted, but the number of subjects or samples is not specified.
     • Physical and Dimensions Testing: Inspection Level S-2, AQL 2.5.
     • FDA 1000 ml. Water Fill Test: Samplings of AQL 2.5, Inspection Level I.
   • Data Provenance: The document implies the testing was conducted by or on behalf of Shandong Zhiwei Medical Products Co., Ltd., based in China, for regulatory submission to the FDA. The nature (retrospective/prospective) of these specific tests is not stated, but they are typically prospective functional and safety tests performed on representative samples of the manufactured device.

3. Number of experts used to establish the ground truth for the test set and qualifications of those experts:

   • This submission is for a patient examination glove, a relatively simple medical device. The "ground truth" for the performance characteristics (e.g., tensile strength, pinholes, residual powder, biocompatibility) is established through standardized laboratory testing protocols (e.g., ASTM standards) rather than expert consensus on medical images or clinical outcomes.
   • Therefore, the concept of "experts" to establish a ground truth in the way it applies to diagnostic AI devices (e.g., radiologists interpreting images) is not directly applicable here. The "experts" would be the qualified laboratory personnel conducting the tests according to the specified ASTM and FDA protocols, ensuring adherence to the methodology. Their specific number and qualifications (e.g., years of experience in materials testing, chemistry, or toxicology) are not detailed in this summary.

4. Adjudication method for the test set:

   • Adjudication methods like "2+1" or "3+1" are typically used in studies involving human interpretation (e.g., radiologists, pathologists) where disagreements need to be resolved.
   • For the laboratory-based performance and safety testing of this medical device, adjudication in this sense is not directly applicable. The "adjudication" is inherent in the adherence to standardized testing methods (e.g., ASTM standards), where objective measurements are taken, and results are compared against predefined acceptance criteria. Any deviations or borderline results would typically lead to re-testing or investigation according to quality control procedures.

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:

   • No, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI-powered diagnostic devices where human readers (e.g., radiologists) are assisted by AI. This device is a physical patient examination glove and does not involve human interpretation or AI assistance in the same context.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   • No, a standalone algorithm performance study was not done. This device is a physical product, not an algorithm or software. The performance evaluations are based on the intrinsic physical, chemical, and biological properties of the glove itself.

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

   • The "ground truth" for this device's performance is based on objective measurements and standardized test results against established industry and regulatory standards.
     • Physical Properties: Measured values for tensile strength, elongation, length, width, and thickness are the ground truth, compared against "similar" performance of predicate devices and ASTM D5250-06 requirements.
     • Pinhole Detection: The quantifiable count of pinholes in a sample is the ground truth, compared against an AQL of 2.5 (ASTM D5151-06) and the FDA 1000 ml. Water Fill Test.
     • Residual Powder: The measured weight of residual powder is the ground truth, compared against the <2 mg/glove criterion of ASTM D6124-06.
     • Biocompatibility: The observed biological response in irritation and sensitization tests (e.g., "not an irritant," "not a sensitizer") serves as the ground truth, assessed under specific study conditions.

8. The sample size for the training set:

   • This document describes the premarket notification (510(k)) for a physical medical device (patient examination gloves). It does not involve machine learning or AI, and therefore, there is no "training set" in the context of algorithm development. The gloves are manufactured, and samples from production lots are tested to ensure quality and demonstrate substantial equivalence.

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

   • As there is no AI/ML involved and thus no "training set," this question is not applicable. The "ground truth" for the device's performance is established through adherence to the testing methodologies themselves as described in item 7.

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