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The Omega Laboratories Hair Drug Screening Assays are test systems that utilize ELISA assays for the qualitative detection of morphine and related opiates (calibrated with morphine) and oxycodone and hydrocodone (calibrated with oxycodone) at or above 300 pg/mg in head hair samples.

The Omega Laboratories Hair Drug Screening Assay for Opiates, Oxycodone and Hydrocodone provide only preliminary analytical test results. A more specific alternate chemical method must be used in order to obtain a confirmed result. Gas Chromatograph – Mass Spectrometry operating in the selected ion monitoring (SIM) mode or GC/MS/MS in selected reaction mode (SRM) is the preferred method with deuterated internal standards. Other chemical confirmation methods are available. Clinical consideration and professional judgment should be applied to any drug of abuse test result, particularly when preliminary positive results are obtained.

These laboratory developed tests are intended exclusively for in-house professional use only and are not intended for sale to anyone. Omega offers these laboratory developed tests as services to its clients.

The Omega Laboratories Hair Drug Screening Assays for Opiates, Oxycodone and Hydrocodone are test systems using ELISA reagents and micro-plate reader for the qualitative detection of Opiates, Oxycodone and Hydrocodone in hair samples at or above 300 pg/mg.

The provided text describes a 510(k) submission for a hair drug screening assay. The information focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed acceptance criteria and performance data for a new, unique device. Therefore, many of the requested sections (e.g., acceptance criteria table, detailed sample sizes for training/test sets, expert qualifications, adjudication methods, MRMC studies) are not explicitly present in the provided document, as a different type of evidence is required for a substantial equivalence claim for an immunoassay.

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

1. Table of Acceptance Criteria and Reported Device Performance

For devices demonstrating substantial equivalence through comparison to predicate devices, the "acceptance criteria" are generally that the new device's performance characteristics (precision, analytical sensitivity, interference, antibody cross-reactivity, and qualitative results in donor specimens) are "in substantial agreement" with or "substantially equivalent" to the predicate. Specific numerical thresholds for these criteria are not provided in this summary, as is common for this type of submission.

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

• Test Set Sample Size: The document mentions "Results obtained from donor specimens," but does not specify the sample size for these donor specimens.
• Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. Given the nature of a 510(k) submission for a diagnostic assay, it's highly probable these were controlled prospective studies conducted at the manufacturer's or a contract research organization's laboratory, likely within the US, but this is not stated.

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

This type of information is generally not applicable or required for an immunoassay 510(k) submission focused on analytical performance and substantial equivalence. The "ground truth" for a performance study of such a device generally refers to the confirmed analytical result, typically established by a gold standard method (like GC/MS or GC/MS/MS), not by human expert interpretation like in imaging studies.

4. Adjudication Method for the Test Set

Not applicable. As noted above, the "ground truth" for an immunoassay's analytical performance is based on chemical or instrumental confirmation, not human interpretation requiring adjudication.

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

This section is not applicable. The device is an ELISA-based qualitative drug screening assay, not an AI-assisted diagnostic imaging or interpretation tool that involves human readers. Therefore, an MRMC study or AI assistance is irrelevant to this submission.

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

This concept is not directly applicable in the AI sense. The device is a standalone assay system (reagents and micro-plate reader) that provides a qualitative result. Its performance is determined analytically, independent of direct human interpretation influencing the primary result. However, it provides "preliminary analytical test results," explicitly stating that "A more specific alternate chemical method must be used in order to obtain a confirmed result." This implies it's a screening tool, not a definitive diagnostic, and further human interpretation of confirmed results would occur elsewhere.

7. The Type of Ground Truth Used

The document explicitly states: "A more specific alternate chemical method must be used in order to obtain a confirmed result. Gas Chromatograph – Mass Spectrometry operating in the selected ion monitoring (SIM) mode or GC/MS/MS in selected reaction mode (SRM) is the preferred method with deuterated internal standards."

Therefore, the ground truth for this device's performance comparison is established using confirmatory analytical methods (GC/MS or GC/MS/MS), which are considered the gold standard for drug detection. This is not expert consensus, pathology, or outcomes data in the typical sense for medical imaging or clinical trials.

8. The Sample Size for the Training Set

The document is for a 510(k) of an ELISA assay, not an AI/ML model. Therefore, the concept of a "training set" for model development is not applicable in the way it would be for an AI device. The assay development would involve optimizing reagent formulations and protocols, but not "training" a machine learning algorithm on data.

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

As explained in point 8, the concept of a "training set" is not applicable in the context of this device's chemistry-based assay development.

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