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The Dimension Vista™ Total Iron Binding Capacity (TIBC) Calibrator is an in vitro diagnostic product for the calibration of the Total Iron Binding Capacity (TIBC) method on the Dimension Vista™ system.

The Dimension Vista™ Total Iron Binding Capacity (TIBC) Calibrator is a liquid, bovine albumin based product containing human transferrin. The kit consists of 3 vials, each containing 1.0 mL.

Here's an analysis of the provided text regarding the acceptance criteria and study information for the Dimension Vista™ Total Iron Binding Capacity (TIBC) Calibrator.

Please note: The provided text is a 510(k) summary for a calibrator, not a diagnostic device that performs interpretations. Therefore, many of the typical questions for AI/diagnostic device studies (like sample size for test/training sets, number of experts for ground truth, adjudication methods, MRMC studies, standalone performance, and ground truth types like pathology/outcomes data) are not applicable in this context. The document focuses on demonstrating **substantial equivalence** to a predicate device, primarily through comparison of their characteristics and intended use.

Acceptance Criteria and Device Performance

For a calibrator like the Dimension Vista™ TIBC Calibrator, the "acceptance criteria" isn't typically phrased in terms of sensitivity, specificity, or accuracy against a disease state. Instead, acceptance is based on demonstrating that its performance is equivalent to a legally marketed predicate device for its intended use (calibration). The "reported device performance" is implicitly shown through the comparison table and the conclusion of substantial equivalence.

Table of Acceptance Criteria and Reported Device Performance

Given this context, the "acceptance criteria" can be inferred as matching the key characteristics and performance expectations of the predicate device for calibrator functionality.

Study Information (Based on Available Text):

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

   • Not Applicable. This is a 510(k) submission for a calibrator, not a diagnostic device evaluating patient samples. The "test set" would refer to internal validation of the calibrator's performance against expected values, but details on sample size or provenance for such internal testing are not provided in this summary. The focus is on comparison to a predicate.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

   • Not Applicable. There is no "ground truth" derived from expert consensus on patient data described for this calibrator submission. The ground truth for a calibrator relates to its chemical composition and its ability to produce expected instrument responses, which is typically validated through internal quality control and comparison to a primary standard (NIST Iron Standard SRM 937).

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

   • Not Applicable. No adjudication method for a "test set" of patient data is relevant here.

4. 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 is not an AI-assisted diagnostic device.

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

   • Not Applicable. This is not an algorithm or AI device.

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

   • The "ground truth" for the calibrator's value is established by its primary standard: NIST Iron Standard SRM 937. This refers to a standard reference material from the National Institute of Standards and Technology, which provides a highly accurate and certified concentration of iron. The calibrator's assigned values are traceable to this primary standard.

7. The sample size for the training set

   • Not Applicable. This is a calibrator, not a machine learning device that requires a training set.

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

   • Not Applicable. No training set is used.

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