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The Glucose Hexokinase reagent set is intended to be used in a diagnostic laboratory setting by qualified laboratory technologists for the quantitative determination of glucose in human serum and plasma on the Pointe 360 Analyzer. It is for In Vitro diagnostic use only. The determination of glucose in serum and plasma is for use in the diagnosis and treatment of diabetes mellitus.

The Pointe 360 is a computerized bench top laboratory instrument. It is capable of automating all stages of assay processing that involve incubation, reagent delivery, mixing, optical reading, calculating, data storage and reporting within specified limits. The glucose reagent set for the Pointe 360 is an assay for the determination of glucose in plasma or serum.

The provided 510(k) summary focuses on demonstrating substantial equivalence of the "Pointe 360 Glucose Hexokinase Reagent Set" to a predicate device, the "Roche Diagnostics Glucose/HK on the Hitachi 917". The studies described are for the performance of an in vitro diagnostic reagent and its associated analyzer, not an AI/ML powered device. As such, many of the requested categories for AI/ML device studies (e.g., number of experts for ground truth, adjudication method, MRMC studies, training set details) are not applicable to this document.

However, I can extract information related to the acceptance criteria and the device performance for this specific medical device, which is a chemical reagent and an analyzer.

Here's a breakdown of the relevant information:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly define "acceptance criteria" in a separate section with pass/fail thresholds. Instead, it presents performance characteristics for the proposed device (Pointe 360 Glucose Hexokinase Reagent and Pointe 360 analyzer) and compares them to the predicate device (Roche Diagnostics Glucose/HK and Hitachi 917). The implicit acceptance criteria are that the Pointe 360's performance is comparable to or better than the predicate device, or within generally accepted analytical performance ranges for glucose measurement.

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

The document does not explicitly state a test set in the way an AI/ML model would have one. Instead, it refers to "samples" for precision studies and "serum/plasma" for correlation.

• Precision Studies:
  • For "Within Day" precision, Sample 1, 2, and 3 have means of 81, 276, and 468 mg/dl respectively. The 'N' value is missing for the proposed device's precision, but for the predicate, it states 'N=63' for all samples. This suggests a number of replicates were run for each sample level to calculate SD and CV.
  • For "Day to Day" precision, Sample 1, 2, and 3 have means of 81, 261, and 451 mg/dl respectively. Again, 'N' is missing for the proposed device, while 'N=63' for the predicate.
• Interference Study: The document states "This data was generated using the Pointe 360 analyzer." but does not specify the number of samples or the provenance (e.g., country of origin, retrospective/prospective).
• Correlation Study: No specific sample size (N) is given for the correlation studies for either serum or plasma.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

Not applicable. This is an in vitro diagnostic device that measures a chemical analyte (glucose). "Ground truth" is established by the reference method or accuracy of the measurement itself, not by expert interpretation.

4. Adjudication method for the test set

Not applicable. There is no human interpretation or adjudication involved in the output of this device.

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 is not an AI/ML powered device, nor does it involve human readers interpreting cases.

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

The device itself is a standalone instrument for performing the glucose assay. Its performance characteristics listed (linearity, LOD, precision, correlation, interference) are all measures of the algorithm/reagent/analyzer system's intrinsic performance. There isn't an "algorithm only" component separate from the physical device in this context.

7. The type of ground truth used

The "ground truth" implicitly used for an in vitro diagnostic device measuring glucose would typically be:

• Reference method comparison: Comparison against a highly accurate and precise reference method (e.g., isotope dilution mass spectrometry (IDMS) or specific enzymatic methods). While not explicitly stated here, "Correlation" studies often imply comparison to a gold standard or a well-established method. The regression equations provided (e.g., y = 0.960x + 3.1) suggest a comparison against another method (where 'x' would likely be the reference method result).
• Known concentration samples: For linearity, precision, and limit of detection, samples with known, prepared concentrations of glucose are used.

8. The sample size for the training set

Not applicable. This is not an AI/ML device, so there is no "training set." The development of the reagent and analyzer involves chemical engineering and instrument design, not machine learning training.

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

Not applicable. No training set for an AI/ML model.

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