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

Powder Free Vinyl Patient Examination Gloves (Yellow, White) are Patient Examination Gloves, Disposable, single use only and non-sterile. The gloves are made of vinyl materials and are powder free. The physical and performance characteristics of the devices meet all requirements of ASTM D5250-06 (Reapproved 2015) Standard Specification For Poly (Vinyl Chloride) Gloves For Medical Application.

This document is a 510(k) Pre-Market Notification for Powder Free Vinyl Patient Examination Gloves. It focuses on demonstrating substantial equivalence to a predicate device, rather than proving the device meets acceptance criteria through clinical studies in the typical sense of AI/software devices. The "acceptance criteria" and "study that proves the device meets the acceptance criteria" here refer to compliance with recognized standards and performance thresholds for examination gloves.

Here's a breakdown of the requested information based on the provided text, interpreted in the context of a medical glove submission:

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)

• Sample Size for Test Set: The sample sizes are indicated by the "Inspection Level and AQL" (Acceptable Quality Limit) for most physical and performance tests, such as S-2, AQL4.0, and G-I, AQL2.5. For Residual Powder, the sample size was N=5. These AQL levels refer to sampling plans (e.g., ISO 2859-1 or ANSI/ASQ Z1.4), where a specific number of units are inspected from a lot, and the lot is accepted or rejected based on the number of defects found. The exact number of units tested isn't explicitly stated but would be determined by the AQL tables for the lot size.
• Data Provenance: The manufacturing entity is Shanxi Hongjin Plastic Technology Co., LTD, located in China. The testing would have been conducted by or on behalf of this company. The data is prospective in the sense that it's generated for the purpose of demonstrating compliance for this submission.

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 product is a medical device (patient examination gloves) tested against recognized physical, chemical, and biological standards. "Ground truth" in this context is established by the accepted scientific methodologies and criteria outlined in the ASTM and ISO standards, rather than expert human interpretation in the way it applies to diagnostic AI. Therefore, there are no "experts" in the sense of clinicians establishing ground truth for individual cases. The ground truth is the objective measurement against the specified standard requirements.

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

Not applicable. Adjudication methods like 2+1 or 3+1 are used for resolving discrepancies in expert interpretations (e.g., in medical image reading). For device performance testing against quantitative standards, the measurements are objective.

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

Not applicable. This device is a physical product (medical glove), not an AI or software algorithm. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not relevant.

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

Not applicable. This is not an algorithm or AI device.

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

The "ground truth" for the performance tests in this submission is the objective measurement against scientifically established and recognized standards (e.g., ASTM D5250-06 for physical properties, specific thresholds for residual powder, and standard biocompatibility tests for irritation and sensitization). It is not pathology, outcomes data, or expert consensus on a diagnostic interpretation.

8. The sample size for the training set

Not applicable. This is not a machine learning/AI device, so there is no training set in that context. Statistical sampling is used for quality control during manufacturing and for conformity testing.

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

Not applicable. As there is no training set for a machine learning model, this question is not relevant.

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

Classified by FDA's General Hospital and Personal Use Device panel as Class I, 21 Classince by I DA's Gelleral Hoopication Glove, 80 LYZ, and meets all requirements of ASTM standard D-5250-06e1.

The provided text describes a 510(k) submission for Supermax Plastic Products Co., Ltd.'s Powder Free Vinyl Patient Examination Gloves. It details the device, its intended use, comparison to a predicate device, and the non-clinical tests performed to demonstrate substantial equivalence.

Here's an analysis based on your request, highlighting that this is for a medical device (glove), not an AI/software device, so many of your requested fields related to AI/software studies are "Not Applicable".

Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size:
  • For the FDA 1000 ml Water Fill Test: Sampling based on AQL 2.5, Inspection Level I (specific number of gloves not provided, but determined by the AQL standard).
  • For general physical/dimensional testing: Inspection level S-2, AQL 4.0 (specific number of gloves not provided, but determined by the AQL standard).
  • For Primary Skin Irritation and Skin Sensitization: Not specified, but standard biological tests would use a certain number of subjects/samples.
  • For Residual Powder Test: Not specified, but based on ASTM D-6124-06.
• Data Provenance: Not explicitly stated, but the tests were conducted by the manufacturer (Supermax Plastic Products Co., Ltd. in China) and likely refer to internal testing data. It is a retrospective evaluation of manufactured batches.

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

• Not Applicable. This is a physical device (glove) evaluation, not an AI/software diagnostic tool. Ground truth is established by objective physical and chemical measurements (e.g., water leak, powder weight) and biological tests, not expert interpretation of outputs.

4. Adjudication method for the test set

• Not Applicable. As per point 3, ground truth is established by objective measurements and standardized tests, not by expert consensus requiring adjudication.

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

• Not Applicable. This is a physical device (glove); there are no "human readers" or "AI assistance" in this context.

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

• Not Applicable. This is a physical device (glove), not an algorithm.

7. The type of ground truth used

• The ground truth is established through adherence to recognized industry standards (ASTM D-5250-06e1, ASTM D-6124-06) and FDA-prescribed test methods (FDA 1000 ml Water Fill Test).
• It involves objective physical measurements, chemical analysis, and biological safety testing (primary skin irritation and sensitization).

8. The sample size for the training set

• Not Applicable. This is not an AI/ML model that requires a training set. The "training" in manufacturing would refer to process control and quality assurance over production runs, not data for an algorithm.

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

• Not Applicable. As per point 8, there is no training set in the context of an AI/ML model for this device.

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