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High Range Peroxide™ Test Strip is intended for use as an efficacy test strip for measuring effective levels of peroxide in Renalin® disinfectant solution. Each time when the kidney unit is reprocessed with peroxide disinfectant solution such as Renalin® , the reprocessing solution should be monitored with the High Range Peroxide™ Test Strip to assure that Renaline concentration is 3% as it is intended to be. After the kidney is placed under storage until next use, the Renaling concentration in the disinfectant solution should be checked again before rinsing with the strip to assure that Renalins concentration has been maintained at 1% or higher. If the Renaling concentration drops below 1%, the kidney unit has to be either reprocessed or discarded.

High Range Peroxide™ Strip is a paper based dry chemistry reagent strip. It consists of a single reagent pad, 0.2x0.2 inch square, adhered to one end of a 0.2x2.5 inch plastic handle with a double sided adhesive. It is a self-contained, ready to use dip-andread reagent strip without additional reagent. The strip is packaged in 50 or 100's in bottles. Color blocks corresponding to Renalin8 concentrations of 0.2, 0.5, 1.0, 2.0 and 3.0 % are printed on the bottle label. Quantitative estimation of Renalink concentration can be made by comparing the strip color to the color blocks.

Here's a breakdown of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The provided document is a 510(k) summary for a reagent strip, not a detailed clinical study report. Therefore, the "reported device performance" is described qualitatively as part of a substantial equivalency argument rather than with specific statistical metrics (e.g., sensitivity, specificity, accuracy percentages) which would be typical for more complex devices or software.

2. Sample Size Used for the Test Set and the Data Provenance

The document does not explicitly state a quantitative sample size for a "test set" in the context of a formal study with statistical reporting. The evaluation appears to be based on the chemical principle and a comparison to a predicate device.

• Data Provenance: Not explicitly stated as "country of origin" or "retrospective/prospective." The comparison is a direct technological comparison and statement of equivalence rather than a field test with patient data.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

This information is not provided. Given the nature of a chemical reagent strip, "ground truth" would likely refer to known, prepared concentrations of Renalin® solution, verified by analytical chemistry methods, rather than expert interpretation of clinical data.

4. Adjudication Method for the Test Set

This information is not provided. Adjudication methods like 2+1 or 3+1 typically apply to studies where human interpretation of medical images or other complex data is involved. For a dip-and-read chemical strip, the "reading" is a visual comparison to color blocks.

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

• No, an MRMC study was not conducted. This type of study is relevant to imaging devices or software where human readers interpret results, often with and without AI assistance. This device is a simple chemical reagent strip.

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

• This question is not applicable in the usual sense. The device is a "dip-and-read" reagent strip, meaning there is an inherent human "in-the-loop" visual comparison to color blocks. There is no automated algorithm that performs a standalone reading. The performance is the "human-in-the-loop" performance based on visual comparison.

7. The Type of Ground Truth Used

• The ground truth would be based on known, prepared concentrations of Renalin® solution (or equivalent peroxide levels). These concentrations would be analytically verified to ensure accuracy. The document refers to "Renalin® concentrations of 0.2, 0.5, 1.0, 2.0 and 3.0 %" as the basis for the color blocks, implying these are the known target values for the comparison.

8. The Sample Size for the Training Set

• This information is not provided and is generally not applicable for a simple chemical reagent strip of this nature. "Training set" typically refers to data used to train machine learning algorithms.

9. How the Ground Truth for the Training Set Was Established

• This information is not applicable as there is no "training set" in the context of machine learning. The "ground truth" for the development of the device (i.e., establishing the color scale for different concentrations) would have involved preparing solutions of known Renalin® concentrations and observing the color development on the strip.

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WaterCleck™ CP Reagent Strip is intended to be used in detecting residual chlorine or peroxide in dialysis water. It also is used for detection of effective high level of chlorine in the disinfecting solution.

WaterCleck™ CP Reagent Strip is a paper based dry chemistry reagent strip. It consists of a single reagent pad, 0.2x0.2 inch square, adhered to one end of a 0.2x2.5 inch plastic handle with a double sided adhesive. The chemistry of the regent strip allows the same single reagent pad to react with either chlorine or peroxide. It is self-contained and is ready to use as a dip-and-read reagent strip without other additional reagent.

The provided text describes a medical device called "WaterCheck™ CP Reagent Strip for Chlorine and Peroxide." It focuses on its intended use, technological comparison to existing products, and a statement of substantial equivalence. However, the document does not contain the detailed acceptance criteria and study information typically found in a comprehensive medical device submission that would allow for a complete answer to your request.

Specifically, the document lacks:

• A table of acceptance criteria with reported device performance values.
• Details on sample sizes for test sets, data provenance, or information about training sets.
• Information on experts used to establish ground truth, adjudication methods, or MRMC studies.
• Data regarding standalone algorithm performance or the type of ground truth used in a formal study.

Therefore, I cannot fully answer your request with the provided information.

However, based on the limited information about performance claims, I can infer what might be considered acceptance criteria based on its comparison to predicate devices, but this is a reconstruction and not explicitly stated in the document.

Inferred Potential Acceptance Criteria and Reported Device Performance (Based on Substantial Equivalence Claims):

Missing Information (Cannot be extracted from the provided text):

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not available.
2. 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): Not available.
3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not available.
4. 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 not an AI device or an imaging study requiring human reader performance).
5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable (this is a chemical reagent strip, not an algorithm).
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not explicitly stated, but for chemical detection, the "ground truth" would typically be established by laboratory methods using known concentrations of chlorine or peroxide.
7. The sample size for the training set: Not applicable (this is a chemical reagent strip, not a machine learning model requiring a training set).
8. How the ground truth for the training set was established: Not applicable.

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