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Resert™ XL HLD is a ready -to-use liquid chemical germicide. The product is a 2.0% nominal hydrogen peroxide solution. The minimum recommended concentration (MRC) is 1.5%. Resert XL HLD is intended to be used by health care practitioners in clinical settings as a ready to use liquid chemical disinfectant for the high level disinfection of semi-critical medical devices for which alternative methods of terminal reprocessing are not suitable or available.

High Level Disinfectant: Resert™ XL HLD High Level Disinfectant is a high level disinfectant when used or reused undiluted for a maximum of 21 days at a minimum temperature of 20℃ (68°F) for a minimum immersion time of 8 minutes.

Resert XL HLD High Level Disinfectant is a nominal 2.0% hydrogen peroxide solution, buffered with furoic acid and phosphorous acids. Hydrogen peroxide is the sotive ingredient in Resert XL HLD, which exerts its germicidal action via a strong oxidation reaction of cellular components.

This document (K091022) is a 510(k) summary for a high-level disinfectant, Resert™ XL HLD. It asserts substantial equivalence to a previously cleared device (K080420) and mainly concerns a labeling change. As such, it does not contain the kind of detailed study information (like a description of a clinical study, sample sizes, ground truth establishment, or expert adjudications) that would be present for a novel medical device approval based on performance data.

Here's an analysis based on the provided text, outlining why most of the requested information cannot be found:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Not Applicable. This submission is for a labeling change on a chemical disinfectant, asserting substantial equivalence to an existing product. It does not involve a "test set" of patient data or clinical images. The "test" here is effectively a review by the FDA for substantial equivalence based on product formulation and labeling.

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

• Not Applicable. As there is no "test set" of patient data, there is no ground truth established by experts in the context of diagnostic accuracy. Substantial equivalence is determined by the FDA.

4. Adjudication method for the test set

• Not Applicable. No test set requiring adjudication is described.

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 chemical disinfectant, not an AI-powered diagnostic device. An MRMC study is irrelevant to this submission.

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

• Not Applicable. This is a chemical disinfectant, not an algorithm.

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

• Not Applicable. The "ground truth" for this submission is the established safety and efficacy profile of the predicate device (Resert™ XL HLD, K080420), to which the current device is deemed "identical" except for the labeling change. The FDA's substantial equivalence determination functions as the "truth" in this regulatory context.

8. The sample size for the training set

• Not Applicable. This device is a chemical disinfectant, not a machine learning model. There is no concept of a "training set."

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

• Not Applicable. As there is no training set, this question is irrelevant.

In summary: The provided document is a 510(k) submission for a minor labeling change for a high-level disinfectant. It relies on the substantial equivalence principle, meaning the device's safety and effectiveness are established by comparing it to a legally marketed predicate device. Therefore, it does not contain the detailed clinical study data typically associated with a novel device's performance evaluation against specific acceptance criteria using a test set, expert ground truth, or AI performance metrics.

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