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

Powder Free Nitrile Examination Glove, Black and dual color White-Blue are Class 1 Patient Examination Gloves. The dual color White-Blue is made dual color by double dipping. They are ambidextrous and come in different sizes, Extra Small, Small, Medium, Large and Extra Large, Double Extra-Large and Triple Extra-Large.

This document describes the premarket notification (510(k)) for Powder Free Nitrile Examination Gloves (Black and Dual Color White-Blue) from Careplus (M) SDN BHD. This is for an examination glove, a Class I reserved device (21 CFR 880.6250) intended for medical purposes to prevent contamination between patient and examiner.

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state the specific sample sizes used for each test. Instead, it indicates that the "performance test data from the non-clinical test met the specifications from ASTM testing methodology." ASTM standards for examination gloves (like D6319-19) typically define sampling plans for various tests (e.g., AQL for pinholes).

The data provenance is not specified beyond being generated by the device manufacturer, Careplus (M) SDN BHD, located in Seremban, Negeri Sembilan Darul Khusus, Malaysia. The studies are prospective in the sense that they were conducted to test the performance of the specific subject devices.

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 section is not applicable as the device is an examination glove, not an AI or diagnostic imaging device that requires expert interpretation to establish a "ground truth" for a test set. Its performance is evaluated against physical and chemical specifications and biocompatibility standards.

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

This section is not applicable. The performance evaluation for examination gloves typically involves objective measurements and scientific testing against established standards, not subjective adjudication by experts on a test set as would be found in AI/diagnostic studies.

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 section is not applicable. The device is an examination glove, not an AI-assisted diagnostic tool. Therefore, MRMC studies are irrelevant to this submission.

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

This section is not applicable. The device is a physical product (an examination glove), not an algorithm or AI system.

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

The "ground truth" for this device's performance is established by objective measurements and adherence to recognized international standards such as ASTM D6319-19 (Standard Specification for Nitrile Examination Gloves for Medical Application) and ISO 10993 (Biological evaluation of medical devices for biocompatibility). For example:

• Pin-hole detection is measured against an Acceptable Quality Level (AQL).
• Physical properties like tensile strength, elongation, and dimensions are tested against minimum/maximum values specified in the ASTM standard.
• Biocompatibility is assessed through standardized tests (e.g., skin irritation, sensitization, acute systemic toxicity, in vitro cytotoxicity) as per ISO 10993, with predefined criteria for "no irritant response," "no skin sensitization effect," etc.

8. The sample size for the training set

This section is not applicable. The device is a physical product, not a machine learning model, so there is no "training set."

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

This section is not applicable for the same reasons as point 8.

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