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Cannabinoids II (THC II) is an in vitro diagnostic test for the qualitative and semiquantitative detection of cannabinoids in human urine on automated clinical chemistry analyzers at cutoff concentrations of 20 ng/ml, 50 ng/ml and 100 ng/ml. Semi-quantitative test results may be obtained that permit laboratories to assess assay performance as part of a quality control program.

The ONLINE DAT II Cannabinoids II (THC II) assay is an in vitro diagnostic test for the qualitative and semi-quantitative detection of cannabinoids in human urine on automated clinical chemistry analyzers at cutoff concentrations of 20 ng/ml, 50 ng/ml and 100 ng/ml. Semi-quantitative test results may be obtained that permit laboratories to assess assay performance as part of a quality control program.

The ONLINE DAT II Cannabinoids II assay is based on the kinetic interaction of microparticles in a solution (KIMS) as measured by changes in light transmission. In the absence of sample drug, soluble drug-polymer conjugates bind to antibody-bound microparticles, causing the formation of particle aggregates.

When a urine sample containing the drug in question is present, this drug competes with the conjugate-bound drug derivative for microparticle-bound antibody. Antibody bound to sample drug is no longer available to promote particle aggregation, and subsequent particle lattice formation is inhibited.

As the aggregation reaction proceeds in the absence of sample drug, the absorbance increases. Conversely, the presence of sample drug diminishes the increasing absorbance in proportion to the concentration of drug in the sample. Sample drug content is determined relative to the value obtained for a known cutoff concentration of drug.

This is an in vitro diagnostic device (IVD) for detecting cannabinoids in human urine. The provided text is a 510(k) summary, which outlines the device's information, intended use, and comparison to a predicate device. The information needed to fully respond to the request, particularly detailed acceptance criteria and a comprehensive study for proving device performance, is not available in the provided text. IVD submissions typically focus on analytical performance data (e.g., sensitivity, specificity, accuracy, precision, linearity, interference studies) rather than the study design elements usually associated with imaging or AI-driven diagnostic devices.

However, based on the available information, here's what can be extracted and inferred:

1. Table of Acceptance Criteria and Reported Device Performance

Specific numerical acceptance criteria are not explicitly stated in the provided 510(k) summary. For IVDs like this, acceptance criteria would typically involve demonstrating:

• Accuracy: The ability to correctly identify positive and negative samples at specified cutoffs.
• Precision: The reproducibility of results.
• Specificity: The absence of interference from other substances.
• Sensitivity: The lowest concentration detectable.

The "reported device performance" is broadly stated as the device being "substantially equivalent" to the predicate device, Abuscreen OnLine Cannabinoids assay (K983701). Substantial equivalence implies that the new device performs at least as well as the predicate device for its intended use. Without the specific performance data from the K030213 submission or the predicate, a detailed table cannot be created.

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

The provided text does not include information on the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). This type of detail is typically found in the "Performance Data" or "Analytical Performance Characteristics" sections, which are not present in this summary.

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

This information is not applicable or not provided in the context of this IVD device. For a chemical assay, "ground truth" for a urine sample is typically established by definitive analytical methods (e.g., Gas Chromatography/Mass Spectrometry - GC/MS) or by spiking known concentrations of the analyte into urine, rather than by human expert interpretation.

4. Adjudication Method for the Test Set

This information is not applicable or not provided. Adjudication methods (like 2+1, 3+1) are typically used in studies involving human interpretation of medical images or clinical scenarios, not for chemical assays where the output is a quantitative or qualitative chemical measurement.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is relevant for evaluating the impact of an AI system on human reader performance, which is not the purpose of this chemical assay.

6. If a Standalone Study (Algorithm Only Without Human-in-the-Loop Performance) Was Done

No, a standalone "algorithm only" study as typically understood for AI/imaging devices was not done. This device is a chemical assay, and its performance is inherently standalone in the sense that the assay itself generates the result without human interpretation of a complex signal that would then be compared to human performance. The assay's performance would be evaluated analytically.

7. The Type of Ground Truth Used

While not explicitly stated, for in vitro diagnostic tests like this, the ground truth for performance studies is typically established by:

• Reference Method Analysis: Using highly accurate and precise analytical methods (e.g., GC/MS for cannabinoids) to determine the true concentration of the analyte in samples.
• Spiked Samples: Using urine samples that have known, precisely measured quantities of the target analyte added to them.

8. The Sample Size for the Training Set

The provided text does not include information on the sample size for any training set. For an IVD like this, there isn't a "training set" in the machine learning sense. Instead, product development involves optimizing reagents and reaction conditions, which might involve iterative testing, but not a formally defined "training set" with ground truth in the way AI models are trained.

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

As there is no "training set" in the AI sense, this question is not applicable. The "ground truth" for optimizing the assay's performance during development would involve knowing the true concentration of cannabinoids in the samples used for optimization, likely established through spiking or reference methods as described in point 7.

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