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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.

Powder-free Vinyl Patient Examination Gloves, Yellow Color. A disposable device intended for medical purposes that is worn upon the examiner's hands or finger. Prevent contamination between patient and examiner. Cover the hand and wrist area. Covers have separate sheaths or openings for each finger and the thumb.

This document is a 510(k) summary for a medical device: Powder-free Vinyl Patient Examination Gloves, Yellow Color. It focuses on demonstrating substantial equivalence to a predicate device. Therefore, the "device" in this context is the glove itself, not an AI or software-based medical device.

As a result, most of the requested information regarding AI/ML device studies (such as sample sizes for test/training sets, data provenance, number/qualifications of experts for ground truth, adjudication methods, MRMC studies, standalone performance, and how ground truth for training was established) is not applicable to this type of medical device filing.

However, I can extract the acceptance criteria and reported performance for the physical characteristics of the gloves based on the provided text.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are generally "Confirmed with ASTM D5250-06" or "Meet ASTM D5250-06" for physical properties and "Meet ASTM D6124-06" for residual powder. For biocompatibility, the criteria are "Not a sensitizer" and "Not an irritant." The reported device performance indicates that the proposed device meets these standards.

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

The document mentions "Inspection Level S-2, AQL 2.5" for ASTM-D-5250-06 testing and "samplings of AQL 2.5, Inspection Level I" for the 1000 ml. Water Fill Test. These are sampling plans/Acceptable Quality Limits (AQLs) taken from standards for quality control inspections, which describe the proportion of defective items allowable in a batch and the sample size to be drawn based on batch size. However, the exact numerical sample size (e.g., "n=X gloves") is not explicitly stated.

The data provenance is implied to be from testing conducted by Zibo Ling Yun Medical Products Co. Ltd in China, as it is the submitter's identification. The tests were non-clinical/bench tests.

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 medical device (examination gloves), not an AI/ML device that requires expert-established ground truth for algorithm performance. The "truth" is determined by standardized physical and chemical tests.

4. Adjudication method for the test set

Not Applicable. As above, this is not an AI/ML device requiring expert adjudication. The test results are objective measurements against established standards.

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 medical device and does not involve AI or human readers.

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

Not Applicable. This is a physical medical device and does not involve an algorithm.

7. The type of ground truth used

The "ground truth" for the performance of these gloves is defined by adherence to established international consensus standards (ASTM D5250-06 for physical properties, ASTM D6124-06 for residual powder) and biocompatibility tests (dermal sensitization and primary skin irritation). These are objective, measurable criteria.

8. The sample size for the training set

Not Applicable. This is a physical medical device using established manufacturing and quality control processes, not an AI/ML device that requires a training set.

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

Not Applicable. As above, no training set for an AI/ML model is involved.

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