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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.

Classified by FDA's General and Plastic Surgery Device Panel as Class I, 21 CFR 880.6250, Pre-powder Vinyl Patient Examination Glove, 80LYZ , powdered with Absorbable Dusting Powder, USP, Class III and meets all requirements of ASTM Standard D5250-92.

The provided text describes the submission for a 510(k) premarket notification for "SHANGHAI COSMOS CORP. Pre-powdered Vinyl Examination Gloves". This document focuses on the substantial equivalence of the device to a predicate device and adherence to established standards for medical examination gloves. It is not a study describing AI performance or an AI-powered device. Therefore, most of the questions regarding AI-specific criteria cannot be answered from the provided text.

Here's an analysis based on the information provided, recognizing that the core request for AI-specific criteria is largely unaddressable:

1. A table of acceptance criteria and the 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)

• Test Set Sample Size:
  • For Physical and Dimensions Testing: Inspection Level S-2, AQL 4.0 (specific sample size from this AQL is not provided, but it's a standard sampling plan).
  • For FDA 1000 ml. Water Fill Test: Samplings of AQL 2.5, Inspection Level S-4 (specific sample size from this AQL/Inspection Level is not provided).
  • For Primary Skin Irritation and Skin Sensitization: Sample sizes are not explicitly mentioned.
• Data Provenance: The tests were conducted for "SHANGHAI COSMOS CORP. Production" which is based in Shanghai, China. The document does not specify if the testing was performed in China or elsewhere, or if it was retrospective or prospective. Given it's a pre-market submission, it would be prospective testing performed on the manufactured product.

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 question is not applicable. The device is a physical product (examination gloves), and its performance is evaluated against material standards and explicit physical/biological tests, not by expert consensus on interpretations of data or images.

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

This question is not applicable. Adjudication methods like 2+1 or 3+1 are used for expert review of images or data where subjective interpretation is involved. For physical product testing against objective standards, such adjudication is not relevant.

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 question is not applicable. This is concerning an examination glove, not an AI-powered diagnostic device.

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

This question is not applicable. This is concerning an examination glove, not an AI algorithm.

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

The "ground truth" for this device's performance is defined by:

• ASTM Standard D5250-92: This standard sets objective physical and dimensional properties (e.g., tensile strength, elongation, dimensions, freedom from holes).
• FDA 1000 ml. Water Fill Test: An objective test for pinhole integrity.
• Bio-compatibility testing: Objective tests for primary skin irritation and sensitization.
• 21 CFR requirements: Compliance with regulatory manufacturing and quality standards (e.g., GMPs).

8. The sample size for the training set

This question is not applicable. This is not an AI device that requires a training set.

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

This question is not applicable. This is not an AI device that requires a training set.

Ask a specific question about this device

A glove is worn on the hand of healthcare and similar personnel to prevent contamination between healthcare personnel and the patient's body, fluids, waste or environment.
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.

Classified by FDA's General and Plastic Surgery Device Panel as Class I, 21 CFR 880.6250, Pre-powder Vinyl Patient Examination Glove, 80LYZ, powdered with Absorbable Dusting Powder, USP, Class III and meets all requirements of ASTM Standard D5250-92.

Here's an analysis of the provided text regarding acceptance criteria and study data:

This document is a 510(k) summary for pre-powdered vinyl examination gloves. Medical gloves, in general, are low-risk devices (Class I) and do not typically involve the complex AI/ML-driven analyses you're asking about. Therefore, many of your questions about AI study specifics (effect size of AI assistance, MRMC studies, training set details) are not applicable to this type of device and submission.

The "acceptance criteria" here refer to meeting established industry standards for the physical properties and safety of medical gloves, rather than performance metrics for an AI algorithm.

Acceptance Criteria and Reported Device Performance

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

2. Sample sized used for the test set and the data provenance

• Physical and Dimensions Testing: Inspection Level S-2, AQL 4.0. The exact sample size is not explicitly stated but is determined by the AQL (Acceptance Quality Limit) and inspection level, which refers to a sampling plan from industry standards.
• FDA 1000 ml. Water Fill Test: AQL 2.5, Inspection Level S-4. The exact sample size is not explicitly stated but is determined by the AQL and inspection level.
• Primary Skin Irritation and Skin Sensitization Testing: The sample size for these biocompatibility tests is not explicitly stated in the summary.
• Data Provenance: Not specified, but generally, these tests would be conducted by the manufacturer's quality control or a third-party testing lab. The submission is from Shanghai Chinastar Corp. in Shanghai, China. The testing would have been done prior to the 1999 submission date. These are non-clinical, lab-based tests.

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

N/A. This is a medical device for general use (gloves), not an AI/ML diagnostic or predictive device requiring clinical expert interpretation to establish ground truth for its performance. The "ground truth" for glove performance is adherence to physical specifications and biocompatibility standards measured by laboratory methods.

4. Adjudication method for the test set

N/A. Adjudication methods like 2+1 or 3+1 are used for expert consensus in interpreting clinical data for devices where human interpretation of medical images or symptoms is central to establishing ground truth. This is not relevant for glove testing.

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

N/A. This is not an AI-assisted device.

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

N/A. This is not an AI algorithm.

7. The type of ground truth used

For this device, the "ground truth" is defined by:

• Standard Specifications: Adherence to ASTM D-5250-92 for physical properties (e.g., dimensions, tensile strength, elongation).
• Performance Tests: Results from the FDA 1000 ml. Water Fill Test for pinhole integrity.
• Biocompatibility Testing: Results from Primary Skin Irritation and Skin Sensitization tests.
• Regulatory Compliance: Conformance to FDA Good Manufacturing Practices (GMPs) and relevant 21 CFR regulations.

8. The sample size for the training set

N/A. There is no AI/ML model for this device that would require a training set. The device's characteristics are determined by manufacturing processes and raw materials, not by learning from data.

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

N/A. See above.

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