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Liquichek Urine Toxicology Control is intended for use as a quality control urine to monitor the precision of laboratory urine toxicology screening procedures.

Liguichek Urine Toxicology Controls are prepared from human urine with added drugs of abuse and metabolites of drugs of abuse, preservatives, stabilizers and constituents of animal origin. The control is provided in liquid form for convenience.

The provided text is for a 510(k) premarket notification for a medical device (Liquichek Urine Toxicology Control). A 510(k) submission typically focuses on demonstrating substantial equivalence to a predicate device, rather than conducting new clinical studies to prove performance against specific acceptance criteria.

Therefore, many of the requested elements (e.g., sample sizes for test sets, data provenance, number of experts for ground truth, adjudication methods, MRMC studies, standalone algorithm performance, training set details) are not applicable or not present in this type of regulatory document. This document is a Summary of Safety and Effectiveness, which details the device, its intended use, and how it compares to a legally marketed predicate device. The "Statement of Supporting Data" section primarily addresses stability studies for the control material itself, not the performance of an AI-driven diagnostic device.

Here's an analysis based on the available information:

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

The document does not specify "acceptance criteria" in the sense of performance thresholds for an AI or diagnostic algorithm (e.g., sensitivity, specificity). Instead, it describes claims related to the stability of the control material. The "reported device performance" in this context refers to the validated stability periods.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not applicable / Not provided. This document does not describe a "test set" for evaluating a diagnostic algorithm. The studies mentioned are stability studies for a quality control material.

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)

• Not applicable / Not provided. This is a quality control material, not a diagnostic device requiring expert interpretation for ground truth.

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

• Not applicable / Not provided.

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 done. This device is a quality control material, not an AI-assisted diagnostic tool.

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

• Not applicable / Not provided. This device is a quality control material, not a standalone diagnostic algorithm.

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

• For the stability studies, the "ground truth" would be established by analytical testing using validated methods (e.g., mass spectrometry, immunoassays) to confirm the concentration and integrity of the analytes within the control material over time and under different storage conditions. The document doesn't detail the specific analytical methods, but these are standard lab practices for control materials.

8. The sample size for the training set

• Not applicable / Not provided. There is no "training set" as this is not an AI/ML device.

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

• Not applicable / Not provided.

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