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In vitro diagnostic for the quantitative determination of carbohydrate-deficient transferrin (CDT) in human serum by means of particle-enhanced immunonephelometry using the BN™ II and BN ProSpec® System. The N Latex CDT assay must be run concurrently with the N Antisera to Human Transferrin assay so that the result can be expressed as a relative ratio, i.e., %CDT of the total transferrin. The calculation of %CDT can be used as a tool to identify possible chronic heavy alcohol consumption.

The CDT in the sample competes with CDT-coated polystyrene particles for the bond to specific monoclonal antibodies against human CDT, which are likewise bound to polystyrene particles. In the presence of CDT in the sample, there is no or little aggregation of the polystyrene particles. In the absence of CDT in the sample, the polystyrene particles aggregate. The higher the CDT content in the assay, the lower the scattered light signal. The evaluation is performed by comparison with a standard of known concentration.

The provided text describes the 510(k) summary for the N Latex CDT Kit, which is an in vitro diagnostic device. The study presented focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than defining and meeting new acceptance criteria for standalone performance or comparative effectiveness.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state "acceptance criteria" in the traditional sense of pre-defined thresholds for performance metrics. Instead, it demonstrates performance by comparing the new device (N Latex CDT assay) against a predicate device (Axis-Shield %CDT assay). The criteria for "acceptance" appear to be based on a high correlation and agreement with the predicate.

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

• Sample Size for Test Set: 116 serum samples
• Data Provenance: The document does not specify the country of origin of the data or whether the study was retrospective or prospective. It only states that 116 serum samples were "evaluated."

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

Not applicable. The study does not establish "ground truth" through expert consensus. Instead, it uses a legally marketed predicate device's results as the reference for comparison.

4. Adjudication Method for the Test Set:

Not applicable. There was no expert adjudication process described, as the comparison was against a predicate device's results.

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 an in vitro diagnostic assay, not an AI-assisted diagnostic tool involving human readers. Therefore, an MRMC study or evaluation of human reader improvement with AI assistance is not relevant to this submission.

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

The device's performance, as described by the regression analysis (y = 0.720x + 0.75 %CDT, correlation coefficient 0.99), is essentially its "standalone" performance when compared against the predicate device. It's a chemical assay, so its performance is inherent to the assay itself.

7. The Type of Ground Truth Used:

The "ground truth" in this context is the results obtained from the predicate device (Axis-Shield %CDT Assay) for the same serum samples. This is a form of reference standard comparison rather than ground truth established by pathology or clinical outcomes.

8. The Sample Size for the Training Set:

Not applicable. This is an in vitro diagnostic assay, not a machine learning or AI-based device that typically requires a distinct training set in the same manner. The "training" of such a device generally refers to its chemical formulation and calibration, which is not detailed in terms of a "training set sample size" in this document.

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

Not applicable, as there is no mention or implication of a "training set" in the context of machine learning or AI. The development of an in vitro diagnostic largely relies on chemical and biological principles, calibration, and validation, rather than ground truth established for a training dataset in the AI sense.

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