Search Results

STARTOX™ Drugs of Abuse Screening Test is a one-step lateral flow immunoassay intended for the simultaneous detection of multiple drug analytes in urine. STARTOX™ is intended for use in the qualitative detection of drugs of abuse at the following Substance Abuse Mental Health Services Administration (SAMHSA) recommended levels:

STARTOX™ Drugs of Abuse Screening Test provide only a preliminary qualitative test result. Use a more specific alternate quantitative analytical method to obtain a confirmed analytical result. Gas chromatography/mass spectrometry (GC/MS) is the preferred confirmatory method. Apply clinical and professional judgment to any drug of abuse test result, particularly when preliminary positive results are obtained.

For professional use only.

A lateral flow immunoassay for the detection of drugs of abuse.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Notes on the table:

• The acceptance criteria are the "Substance Abuse Mental Health Services Administration (SAMHSA) recommended levels" at which the device is intended to detect the drugs.
• The reported device performance is a single aggregated metric ("greater than 99% accuracy") stated against a legally marketed device (Medtox Profile II). The document does not provide separate accuracy figures for each compound, nor does it specify the accuracy relative to the SAMHSA cut-off levels in this particular section (though the study involved testing above and below cut-offs for reproducibility). The "greater than 99% accuracy" is essentially the device meeting its substantial equivalence claim against a predicate, implying it performs at least as well.

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

• Sample Size: 90 samples were tested for each drug. This means:
  • 90 samples for Amphetamine.
  • 90 samples for Opiates.
  • 90 samples for Phencyclidine.
  • 90 samples for Cocaine.
  • 90 samples for Cannabinoids.
    Each set of 90 samples consisted of 50 negative specimens and 40 positive specimens.
• Data Provenance: Not explicitly stated regarding country of origin. The study appears to be retrospective, using previously collected samples that were confirmed as drug-free or screened positive and confirmed by GC/MS.

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

• This information is not provided. The ground truth (confirmation of drug presence/absence and quantification) was established by analytical methods (GC/MS and Emit screening), not by human experts.

4. Adjudication Method for the Test Set

• Not applicable as the ground truth was established by analytical methods (Emit screening and GC/MS), not human interpretation.

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

• Not applicable. This device is a lateral flow immunoassay (a point-of-care type test), not an AI-assisted diagnostic device requiring human interpretation in an MRMC study.

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

• This is effectively a standalone study. The device is a diagnostic assay that provides a direct result (positive/negative) without human interpretation as part of the core test mechanism. Its performance was evaluated against analytical gold standards.

7. The Type of Ground Truth Used

• The ground truth was established using analytical methods:
  • Negative samples were "confirmed to be drug free" (method not specified for this confirmation, but implied to be robust).
  • Positive specimens were "screened as positive by Emit, confirmed and quantified by GC/MS." GC/MS (Gas Chromatography/Mass Spectrometry) is explicitly stated as the preferred confirmatory method and the method used for quantification.

8. The Sample Size for the Training Set

• This information is not explicitly provided. As a lateral flow immunoassay, it is a hardware-based diagnostic device, not a machine learning algorithm that typically undergoes a "training phase" with a labeled dataset in the same way. Its development would involve calibrating the assay components and reagents, which isn't typically referred to as a "training set" in this context.

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

• Not applicable for the reasons mentioned in point 8. The "training" or development of such an assay involves chemical and biological engineering to ensure the reagents perform as expected, rather than "ground truth" derived from a specific dataset for algorithmic learning.

Ask a specific question about this device