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510(k) Data Aggregation
(109 days)
The Giardia CELISA can be used to detect Giardia cysts in fecal specimens from persons suspected of having giardiasis.
The kit, which includes ready-to-use reagents, contains microtiter wells coated with monoclonal antibody, positive control reagent, detecting antibody (polyclonal antibody), conjugate (anti-rabbit IgG-peroxidase), substrate, wash solution, and intensifier. The microtiter wells coated with monoclonal antibody "capture" the antigen and the polyclonal antibody serves as the "detecting" antibody. The polyclonal antibody used as the detecting antibody is prepared from hyperimmune antiserum developed in rabbits. The monoclonal antibody used to coat microtiter wells is prepared from mouse ascites fluid.
Here's an analysis of the provided text, addressing your request for acceptance criteria and study details:
Missing Information:
It's important to note that the provided text is a summary of safety and effectiveness, likely from a regulatory submission. It lacks detailed methodological specifics typically found in a full scientific study publication. Many of your requested points cannot be definitively answered from this text alone.
Analysis based on provided text:
Acceptance Criteria and Device Performance
Acceptance Criteria | Reported Device Performance |
---|---|
Correlation with microscopy (conventional staining methods) | >95% correlation |
Correlation with Meridian Premier Giardia lamblia | >95% correlation |
Usefulness for detecting Giardia in fecal specimens | "Useful" (implied by >95% correlation) |
Explanation: The text states, "The results of our clinical evaluations show that the Giardia CELISA exhibits a correlation of >95% when compared with these other methods for detecting Giardia in fecal specimens." This establishes the acceptance criteria implicitly as a correlation of greater than 95% with the comparator methods.
Study Details:
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Sample size used for the test set and the data provenance:
- Sample Size: Not specified in the provided text.
- Data Provenance: Not specified (e.g., country of origin, retrospective or prospective). The text only mentions "clinical evaluations."
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not specified in the provided text.
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Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- Not specified in the provided text.
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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 an immunoassay, not an AI-powered diagnostic tool for human readers. It directly detects antigens. The comparisons are against other diagnostic methods (microscopy and another immunoassay), not against human readers' performance with and without AI assistance.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone comparison was done. The Giardia CELISA themselves, without human interpretation for the result, were compared against microscopy and the Meridian Premier Giardia lamblia. The "correlation of >95%" refers to the direct performance of the CELISA assay.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Comparator methods: The ground truth for the comparison was established by two comparator methods:
- Detection of the organism in fecal specimens by microscopy with conventional staining.
- Detection of Giardia antigen in fecal specimens by the Meridian Premier Giardia lamblia (another approved in vitro diagnostic).
- While microscopy often involves expert interpretation, the text doesn't explicitly state "expert consensus" as the sole ground truth but rather the outcome of these established diagnostic techniques.
- Comparator methods: The ground truth for the comparison was established by two comparator methods:
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The sample size for the training set:
- Not applicable/Not specified. This is an immunoassay, not a machine learning algorithm that requires a "training set" in the conventional sense. Its development would involve reagent optimization and validation, but not a dataset for algorithmic training.
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How the ground truth for the training set was established:
- Not applicable (as it's not an ML algorithm).
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