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The QUANTA Lite™ SLA (soluble liver antigen) kit is an enzyme-linked immunosorbent assay (ELISA) for the semi-quantitative detection of anti-SLA (soluble liver antigen) antibody of the IgG class in human serum. This test is intended to aid in the diagnosis of conditions with elevated levels of anti-SLA antibody including autoimmune hepatitis (AIH).

The QUANTA Lite™ SLA (soluble liver antigen) kit is an enzyme-linked immunosorbent assay (ELISA).

This document is a 510(k) clearance letter from the FDA for the QUANTA Lite™ SLA ELISA device, not a study report. Therefore, it does not contain the detailed information necessary to fully answer the request regarding acceptance criteria and a study proving device performance as it would typically be found in a clinical study report or a 510(k) summary.

However, based on the provided text, I can extract some relevant information and highlight what is missing.

Information Extracted from the Document:

• Device Name: QUANTA Lite™ SLA (Soluble Liver Antigen) ELISA
• Intended Use: Semi-quantitative detection of anti-SLA (soluble liver antigen) antibody of the IgG class in human serum, intended to aid in the diagnosis of conditions with elevated levels of anti-SLA antibody including autoimmune hepatitis (AIH).

Missing Information (and why it's missing from this type of document):

This letter is an FDA clearance, which means the manufacturer submitted data in their 510(k) application demonstrating substantial equivalence to a predicate device. The detailed study results, acceptance criteria, sample sizes, ground truth establishment, etc., would be in the 510(k) Summary or the full 510(k) submission, which is not provided here.

Therefore, I cannot populate the table or answer most of the specific questions.

Placeholder for Answer Structure, if the full 510(k) Summary were available:

Here's how I would structure the answer if the required information were present:

1. Table of Acceptance Criteria and Reported Device Performance

(This would typically outline performance metrics like Sensitivity, Specificity, Agreement with a predicate device, Precision, etc., and the thresholds set for acceptance.)

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

• Sample Size (Test Set): [e.g., N=500 serum samples (250 positive, 250 negative)]
• Data Provenance: [e.g., Retrospective, multi-center study from hospitals in the USA and Europe.]

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

• Number of Experts: [e.g., 3]
• Qualifications: [e.g., Board-certified Rheumatologists/Hepatologists with 10+ years of experience in diagnosing autoimmune diseases, specifically AIH.]

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

• Adjudication Method: [e.g., Consensus among all 3 experts for ground truth; if disagreement, a fourth senior expert was consulted for final decision (3+1 model).]

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 is an in vitro diagnostic (IVD) device (ELISA assay) for antibody detection, not an imaging AI device that assists human readers. Therefore, an MRMC study is not applicable in this context.

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

• Standalone Performance: Yes, an ELISA is inherently a standalone test. The performance metrics (Sensitivity, Specificity, etc.) are based on the device's ability to detect the analyte directly from the sample without human interpretation influence on the result generation, though human interpretation of the result value (e.g., comparing to a cutoff) is part of its intended use.

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

• Ground Truth Type: [e.g., A combination of established clinical diagnosis of autoimmune hepatitis (AIH) based on International Autoimmune Hepatitis Group (IAIHG) criteria, clinical follow-up, and confirmation using a previously FDA-cleared reference method for anti-SLA antibody detection (e.g., Western blot or IFA) which served as the "gold standard" or predicate comparator.]

8. The sample size for the training set

• Sample Size (Training Set): [e.g., N=1000 serum samples for initial development and optimization. Note: For IVDs, "training set" might refer more to method development/optimization than to a machine learning context.]

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

• Ground Truth (Training Set): [e.g., Similar to the test set, established using clinical diagnosis for AIH and/or predicate device results. For optimization, known positive and negative samples, as well as characterized samples with varying antibody titers, would be used.]

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