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Hemoglobin Alc Linearity is intended for use as quality control material to demonstrate linearity throughout the reportable range of Hemoglobin A1c (HbA1c%) for Immunoassay and HPLC test methods using protocols established in individual laboratories.

The HbAIc Linearity Set is prepared from human blood to which stabilizers are added. The control is provided in liquid form for user convenience.

This submission describes a medical device, the Cone Bioproducts' HbA1c Linearity Set, which is a quality control material. As such, the concept of "acceptance criteria" and "device performance" in the context of clinical efficacy (as with an AI diagnostic tool) does not directly apply. The submission focuses on demonstrating substantial equivalence to a predicate device for its intended use as a quality control material to verify linearity for HbA1c testing.

Therefore, many of the requested items (e.g., sample size for test set, ground truth experts, MRMC study, standalone performance) are not relevant to this type of device and its regulatory submission.

Instead, the "acceptance criteria" here are implicitly defined by the demonstration of substantial equivalence to the predicate device, LiniCAL Enzyme Calibration Verifiers (K040535), based on comparison of intended use and physical properties.

Here's an analysis based on the provided document:

1. A table of acceptance criteria and the reported device performance

As this is a quality control material seeking substantial equivalence, the "acceptance criteria" are not based on diagnostic performance metrics but rather on characteristics that establish its equivalence to a predicate. The "reported device performance" is essentially the detailed description of the subject device's characteristics compared to the predicate.

Notes on the table: The "acceptance criteria" column is derived from the characteristics of the predicate device. The submission argues that even with differences (e.g., single vs. multiple analytes, specific methodologies), the subject device is substantially equivalent for its stated intended use (HbA1c linearity) as a quality control material.

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

This information is not applicable and not provided in the submission. The submission is for a quality control material, not a diagnostic or screening device that would typically involve a "test set" of patient data. The demonstration of equivalence relies on comparing product characteristics and intended use, not on a clinical performance study using patient samples.

3. 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)

This information is not applicable and not provided in the submission. There is no "test set" or "ground truth" establishment in the context of clinical interpretation by experts for this type of device.

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

This information is not applicable and not provided in the submission. There is no "test set" requiring expert adjudication.

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

This information is not applicable and not provided in the submission. This is a quality control material, not an AI-assisted diagnostic tool.

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

This information is not applicable and not provided in the submission. This is a quality control material, not an algorithm or AI system.

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

This information is not applicable and not provided in the submission. For a quality control material, the "truth" is established by the known concentration or characteristic of the control material itself, and its ability to demonstrate linearity is assessed through its performance on a laboratory analyzer, not by comparison to external clinical ground truth.

8. The sample size for the training set

This information is not applicable and not provided in the submission. The device is a manufactured product (quality control material), not a machine learning algorithm that requires a "training set."

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

This information is not applicable and not provided in the submission. As above, there is no "training set" for this type of device.

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