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510(k) Data Aggregation
(127 days)
QUANTA LITE SS-A 52 ELISA
The QUANTA Lite™ SS-A 52 ELISA is a semiquantitative enzymelinked immunosorbent assay for the detection of IgG anti-SS-A 52 antibodies in patient sera. The presence of these antibodies, when considered in conjunction with other laboratory and clinical findings, is an aid in the diagnosis of systemic lupus erythematosus, Sjögren's syndrome, systemic sclerosis, polymyositis and dermatomyositis.
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The provided text is a 510(k) premarket notification approval letter for a medical device called "QUANTA Lite™ SS-A 52 ELISA." This document does not contain the detailed acceptance criteria for the device's performance or the full study that proves it meets those criteria in the format requested.
The document states that the FDA has reviewed the submission and determined the device is "substantially equivalent" to legally marketed predicate devices. This means it meets the regulatory requirements for safety and effectiveness based on comparison to existing devices, but the specifics of its performance criteria and the detailed study design (like sample sizes, expert qualifications, etc.) are generally found in the full 510(k) submission which is not provided here.
Therefore, I cannot populate the table and answer the study-related questions based solely on the provided text. The document is an approval letter, not a scientific study report.
However, I can extract the following information that is present:
Device Name: QUANTA Lite™ SS-A 52 ELISA
Indications for Use: The QUANTA Lite™ SS-A 52 ELISA is a semiquantitative enzyme-linked immunosorbent assay for the detection of IgG anti-SS-A 52 antibodies in patient sera. The presence of these antibodies, when considered in conjunction with other laboratory and clinical findings, is an aid in the diagnosis of systemic lupus erythematosus, Sjögren's syndrome, systemic sclerosis, polymyositis and dermatomyositis.
Regulatory Class: Class II
Product Code: OBE
Regulation Number: 21 CFR 866.5100
Regulation Name: Antinuclear antibody immunological test system
Regarding your specific questions, here's what can be inferred or stated about the absence of information:
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A table of acceptance criteria and the reported device performance: This information is not provided in the approval letter. Acceptance criteria for substantial equivalence are based on performance data (e.g., sensitivity, specificity, reproducibility, correlation with predicate) presented in the 510(k) submission, but these specific metrics and their targets are not listed here.
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Sample sizes used for the test set and the data provenance: Not mentioned in this document.
- Data provenance: Not mentioned.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not mentioned in this document.
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Adjudication method for the test set: Not mentioned in this document.
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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: This device is an ELISA kit, which is an in vitro diagnostic (IVD) test, not an AI-powered imaging or diagnostic system that would typically involve human "readers" or an MRMC study comparing human performance with and without AI. So, this question is not applicable to this type of device.
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If a standalone (i.e. algorithm only without human-in-the loop performance) was done: As an IVD test, the "standalone" performance of the assay itself is what is evaluated. However, the details of that evaluation are not in this document.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For IVD assays, ground truth often involves clinical diagnosis confirmed by other established diagnostic criteria or reference methods. The specific type used for this device is not detailed here.
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The sample size for the training set: Not mentioned. (Note: For an ELISA, "training set" is not a standard term as it is for machine learning. The equivalent would be samples used during assay development and validation).
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How the ground truth for the training set was established: Not mentioned.
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