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The MICRO-STRIP FOR MARIJUANA METABOLITES is an immunoassay that qualitatively detects the major Marijuana metabolites (tetrahydrocannabinoids/THC) in urine at the SAMHSA cutoff level of 50 ng/m1. The MICRO-STRIP FOR MARIJUANA METABOLITES is Intended for use by clinical toxicology laboratories, physician offices, drug abuse clinics and law enforcement agencies only.

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This is a 510(k) premarket notification for "MICRO-STRIP FOR MARIJUANA METABOLITES", an immunoassay for qualitatively detecting major marijuana metabolites (tetrahydrocannabinoids/THC) in urine at the SAMHSA cutoff level of 50 ng/ml. The document explicitly states that the device is intended for use by clinical toxicology laboratories, physician offices, drug abuse clinics, and law enforcement agencies. This FDA letter approves the device, meaning it was found substantially equivalent to a predicate device. Often, 510(k) summaries, not the approval letter itself, contain detailed study information. The provided text is an approval letter, not the full submission. Therefore, much of the requested detailed study information is not available in the provided text.

However, based on the nature of the device (an immunoassay detecting a specific metabolite at a cutoff level), we can infer certain aspects of the acceptance criteria and study design, even if the explicit details are missing.

Here's an attempt to answer the questions based on the available information and general knowledge of such devices, highlighting what is directly stated, inferred, or not present.

1. Table of Acceptance Criteria and Reported Device Performance

The provided FDA letter does not include a table of acceptance criteria or specific reported device performance metrics. Such information would typically be found in the 510(k) summary or the full submission, not in the FDA's approval letter.

However, for a qualitative immunoassay like this, the primary performance metrics would likely revolve around:

• Sensitivity: The ability of the test to correctly identify samples containing marijuana metabolites at or above the 50 ng/ml cutoff.
• Specificity: The ability of the test to correctly identify samples not containing marijuana metabolites above the 50 ng/ml cutoff, or to not react with interfering substances.
• Cutoff Performance: Accurate classification of samples around the 50 ng/ml threshold.
• Reproducibility/Precision: Consistency of results when tested multiple times.
• Cross-reactivity: Assessment against various prescription and over-the-counter drugs, as well as structurally similar compounds, to ensure no false positives.

Inferred Acceptance Criteria (Typical for such devices):

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

• Sample Size for Test Set: Not provided in the given text.
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not provided in the given text. For clinical toxicology assays, data is often collected from diverse populations, and can be prospective (collecting new samples) or retrospective (using archived samples with known concentrations).

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

• Number of Experts: Not applicable/Not provided. For a quantitative chemical analytical method like detecting drug metabolites, the "ground truth" is typically established by a highly sensitive and specific reference method, not by human expert consensus or interpretation.
• Qualifications of Experts: Not applicable/Not provided. The "expert" in this context would be the reference laboratory performing the confirmatory testing. This would typically involve highly trained laboratory technicians and chemists using sophisticated analytical instrumentation (e.g., GC/MS or LC/MS/MS).

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

• Adjudication Method: Not applicable/None in the traditional sense. As the ground truth is established by an objective reference method, there's no need for human expert adjudication of discrepancies between the device and the ground truth. Discrepancies would be analyzed to understand the assay's performance characteristics.

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

• MRMC Study: No. This type of study is relevant for imaging devices or AI-driven diagnostic tools where human interpretation is a key component and AI acts as an assist. The MICRO-STRIP device is a qualitative immunoassay, where the result (e.g., a line appearing or not) is largely objective for the user. It does not involve "readers" in the sense of interpreting complex medical images or data, nor does it involve AI assistance for human interpretation.

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

• Standalone Performance: The "MICRO-STRIP FOR MARIJUANA METABOLITES" device is essentially a standalone test. Its performance is evaluated independently against a reference method. It is designed to provide a direct result without a complex human-in-the-loop interpretation or algorithmic intervention beyond the chemical reaction itself. The "human-in-the-loop" component is limited to performing the test correctly and reading the visible result (line or no line).

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

• Type of Ground Truth: The ground truth for immunoassay tests like this is almost universally established by a reference analytical method, typically Gas Chromatography-Mass Spectrometry (GC/MS) or Liquid Chromatography-Mass Spectrometry/Mass Spectrometry (LC/MS/MS). These methods are considered the "gold standard" for quantitative confirmation of drug metabolites in biological matrices due to their high specificity and sensitivity. Samples would be tested by the device and then confirmed by GC/MS or LC/MS/MS to determine the true concentration and presence/absence of the metabolite relative to the 50 ng/ml cutoff.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/Not provided. Immunoassays are not "trained" in the machine learning sense. Their performance is inherent in their chemical design (antibodies, reagents). While R&D involves optimizing reagent concentrations and manufacturing processes, there isn't a "training set" of data in the way an AI algorithm uses it. Performance evaluation studies typically use a "test set" or "validation set" of samples.

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

• How Ground Truth for Training Set was Established: Not applicable. As explained in question 8, there isn't a "training set" for an immunoassay in the typical sense of establishing ground truth for machine learning. The chemical properties and reactivity of the immunoassay are developed through laboratory experimentation and optimization, not data-driven training.

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