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XN CAL is used for the calibration and calibration verification of Sysmex XN series (XN-10, XN-11, XN-20, XN-21, XN-L) analyzers. Assayed parameters include:

WBC (10^3/μL), RBC (10^6/μL), HGB (g/dL), HCT (%), PLT (10^3/ μL), and RET (%)

XN CAL contains the following: stabilized red blood cell component(s), stabilized white blood cell component(s), stabilized platelet component(s), and stabilized nucleated red blood cell component(s) in a preservative medium.

The medical device in question is the XN CAL, a calibrator for cell indices used with Sysmex XN series analyzers. The provided text, a 510(k) summary, describes its intended use, comparison to a predicate device, and results of performance testing.

Here's a breakdown of the requested information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" with numerical targets for each parameter. Instead, it describes general study objectives and conclusions about reproducibility and stability. The "reported device performance" is given as conclusions derived from tests.

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

The document does not specify the exact sample sizes (number of measurements, number of vials, etc.) used for the multi-site precision, single-site precision, open-vial stability, or closed-vial stability studies.

The data provenance is not explicitly stated regarding country of origin. The studies are described as "Multi-Site" and "Single-Site," suggesting they were conducted at different locations. The studies were likely prospective as they were conducted to substantiate product performance claims for the 510(k) submission.

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

This information is not provided in the document. As XN CAL is a calibrator for automated hematology analyzers, "ground truth" would typically refer to the accurately assigned values for the calibrator, which are then used to calibrate the analyzers. This process usually involves highly controlled laboratory methods rather than expert human interpretation in the way, for example, a radiograph would be interpreted.

4. Adjudication Method for the Test Set

This information is not applicable to this type of device and study. Adjudication methods like 2+1 or 3+1 are typically used in studies where human interpretation of medical images or data is involved and discrepancies need to be resolved. For a calibrator, performance is measured objectively against established reference values or reproducible results.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is relevant for diagnostic devices that involve human interpretation of results (e.g., radiology AI), not for a laboratory calibrator.

6. If a Standalone (i.e., algorithm-only without human-in-the-loop performance) Was Done

This question is not directly applicable in the context of XN CAL as it is a physical calibrator solution, not an algorithm. Its performance is evaluated based on its measured values and stability when run on the specified analyzers, which are themselves automated devices. The "algorithm" here would be the instrument's measurement algorithm, and the calibrator helps ensure that algorithm produces accurate results.

7. The Type of Ground Truth Used

The concept of "ground truth" for a calibrator usually refers to the assigned or reference values for the parameters (WBC, RBC, HGB, HCT, PLT, RET) within the calibrator. These values are established through rigorous, highly accurate, and often multi-replicate testing using reference methods or predicate devices, ensuring their accuracy. The document implies that the "intended use" and "consistently reproducible" nature suggest these established values serve as the ground truth.

8. The Sample Size for the Training Set

This information is not provided and is not applicable for this device. The XN CAL is a physical calibration material, not a machine learning or AI algorithm that requires a "training set" of data. The performance studies described (precision, stability) are a form of verification/validation, not model training.

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

This information is not applicable as there is no "training set" in the context of this device.

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XN CAL PF is used for the calibration and calibration verification of Sysmex XN series (XN-10, XN-11, XN-20, XN-21) analyzers. Assayed parameters include: PLT-F (10^3/ uL)

XN CAL PF contains the following: stabilized red blood cell component(s), and stabilized platelet component(s) in a preservative medium.

This document describes the XN CAL™ PF device, a calibrator for cell indices used with Sysmex XN series analyzers. The primary purpose of the submission (K141955) is to expand the compatible analyzers and extend the closed-vial stability.

Here's an analysis of the provided text in relation to acceptance criteria and study details:

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

The document does not explicitly state numerical acceptance criteria for the different studies. Instead, it states that "The resultant data set established that XN CAL PF is safe and effective for its intended use and that the product is stable for the entire product dating. The product fulfills its intended use as instructed in the Instructions for Use." and "Study results show XN CAL PF to be consistently reproducible, substantially equivalent to the predicate products, and stable for the entire product dating."

Based on the studies conducted and the claims made, the inferred performance metrics and outcomes are:

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

The document does not provide details on the specific sample sizes used for the Open-Vial Stability, Closed-Vial Stability, and Precision Performance studies. It also does not specify the country of origin of the data or whether the studies were retrospective or prospective. It only states that "Streck collected product performance data for the following studies".

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 to this device. XN CAL™ PF is a calibrator for laboratory analyzers, not an diagnostic imaging or AI-driven decision support tool that requires expert human interpretation to establish ground truth for a test set in the traditional sense. The "truth" or reference values for a calibrator are established through manufacturing processes and validation against primary reference methods or standards, not through expert consensus on cases.

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

This information is not applicable for the same reasons as #3. Adjudication methods are typically used in clinical studies where human interpretation or diagnoses are being compared, often with an AI system. Ground truth for a laboratory calibrator is determined by its intrinsic properties and manufacturing metrology.

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

An MRMC study is not applicable to this type of device. XN CAL™ PF is a calibrator, not an AI system or software that assists human readers.

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

This concept is not applicable to XN CAL™ PF. It is a physical calibrator product, not a standalone algorithm.

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

The document does not explicitly state how the "ground truth" or reference values for the calibrator were established. However, for a calibrator, the ground truth would typically be established through:

• Manufacturer's certified reference values: The company manufactures the calibrator to specific concentrations/values (e.g., PLT-F) and validates these values using highly accurate and precise reference methods or instruments, often traceable to international standards.
• Traceability: The values are likely traceable to a recognized standard or method for cell enumeration, ensuring accuracy and consistency across different batches and against a predicate.

8. The sample size for the training set

This information is not applicable. XN CAL™ PF is a physical calibrator product; it does not involve machine learning or a "training set" in the context of AI.

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

This information is not applicable for the same reasons as #8.

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XN CAL is used for the calibration and calibration verification of Sysmex XN series (XN-10, XN-11, XN-20, XN-21) analyzers. Assayed parameters include:

WBC (10^3/uL), RBC (10^6/uL), HGB (g/dL), HCT (%), PLT (10^3/ uL), and RET (%)

XN CAL contains the following: stabilized red blood cell component(s), stabilized white blood cell component(s), stabilized platelet component(s), and stabilized nucleated red blood cell component(s) in a preservative medium.

The provided text describes a 510(k) premarket notification for the XN-CAL™ device, a calibrator for cell indices used with Sysmex XN series analyzers. The submission aims to demonstrate substantial equivalence to a legally marketed predicate device.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of formal acceptance criteria with numerical targets. Instead, it discusses the outcomes of various studies to demonstrate the device's performance claims. The key performance claims evaluated are related to stability and precision.

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

The document primarily focuses on the regulatory submission and does not provide specific details regarding the sample sizes or data provenance (e.g., country of origin, retrospective/prospective) for the studies performed. It generally states that "Streck collected product performance data for the following studies: Open-Vial Stability, Closed-Vial Stability, and Precision Performance."

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

This information is not provided in the document. For a medical device calibrator, the "ground truth" would likely be established through highly controlled laboratory assays using reference methods, rather than expert interpretation of images or clinical cases.

4. Adjudication Method for the Test Set

This information is not applicable and not provided. Adjudication methods are typically relevant for studies involving human interpretation or diagnosis, which is not the primary function of a calibrator for cell indices.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

An MRMC comparative effectiveness study was not done. This type of study is relevant for diagnostic devices that involve human interpretation (e.g., radiologists reading images) and the impact of AI on their performance. The XN-CAL™ is a calibrator, which does not involve human interpretation in the same way.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

A standalone performance study was done. The document describes studies on "Open-Vial Stability, Closed-Vial Stability, and Precision Performance" for the XN CAL device itself. These studies assess the intrinsic performance of the calibrator, independent of human interaction during the calibration process beyond following the instructions for use. The device's performance as a calibrator is evaluated based on its ability to provide stable and precise values for the specified hematological parameters.

7. Type of Ground Truth Used

The ground truth used for evaluating a calibrator like XN-CAL™ would typically be established through:

• Reference materials/methods: Highly accurate and traceable reference materials or established laboratory methods (e.g., manual cell counts, spectrophotometric hemoglobin measurements) would serve as the "true" values against which the calibrator is manufactured and tested.
• Assayed values: The calibrator itself comes with "assayed values," which are the target values for each parameter determined through rigorous testing against reference methods.

The document implicitly refers to this by stating the product performance data "established that XN CAL is safe and effective for its intended use" and "consistently reproducible."

8. Sample Size for the Training Set

The document does not mention a "training set" because XN-CAL™ is a calibrator, not an AI/machine learning algorithm that requires training data. Its performance is based on its chemical and biological formulation and manufacturing consistency.

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

As explained above, there is no "training set" for this type of device. The "ground truth" for calibrator values would be established through meticulous laboratory analysis using established reference methods, not through a training process in the AI sense.

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XN CAL PF is used for calibration and calibration verification of Sysmex XN series (XN-10, XN-20) analyzers. The assayed parameter is:

PLT-F (103/pL)

XN CAL™ PF is an in-vitro diagnostic product that contains the following: stabilized red blood cell component(s), and stabilized platelet component(s) in a preservative medium. The product is packaged in polypropylene plastic vials with screw caps containing 3 ml. The vials will be packaged in (5) welled or (1) welled vacuum formed clamshell container with the Instructions for Use / assay sheet. The product must be stored at 2 - 8° C.

Here's a breakdown of the acceptance criteria and study information based on the provided text, while noting the limitations in what can be extracted for a medical device approval that is not based on AI/ML.

Important Note: The provided document is a 510(k) summary for a hematology calibrator (XN CAL™ PF), not an AI/ML powered medical device. Therefore, many of the requested points related to AI/ML (like training sets, experts for ground truth, MRMC studies, standalone performance) are not applicable to this type of device and will not be found in the document. This device is an in-vitro diagnostic product used for instrument calibration.

Acceptance Criteria and Reported Device Performance

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a quantitative table format for performance metrics. Instead, it relies on demonstrating "substantial equivalence" to a predicate device and stability studies.

Study Details (Applicable to XN CAL™ PF)

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

• Sample Size for Test Set: The document does not specify a numerical sample size for the test sets used in the Open-Vial Stability, Closed-Vial Stability, and Precision Performance studies. It only states that "all testing showed" the results.
• Data Provenance: Not specified in the document. The studies were likely conducted internally by Streck.

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

• Not Applicable. This is a calibrator for a hematology analyzer, not a diagnostic device requiring human expert ground truth for interpretation of images or patient data. Its "ground truth" is its own verified composition and stability, and its performance is evaluated against the instrument's measurements.

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

• Not Applicable. No human adjudication is involved for this type of 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 or one that involves human readers interpreting cases.

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

• Not Applicable. This is not an algorithm. The "performance" of the device is its ability to properly calibrate the instrument and remain stable.

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

• For this device, the "ground truth" for its performance evaluation would likely be:
  • Defined values/concentrations for the stabilized red blood cell and platelet components (for calibration accuracy).
  • Time-based measurements against predefined stability limits (for open and closed vial stability).
  • Repeatability measurements (for precision performance).
  • These are internal quality control and validation metrics, not external clinical ground truth derived from human experts or pathology.

8. The sample size for the training set:

• Not Applicable. This is not an AI/ML device that requires a "training set."

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

• Not Applicable. As there is no training set for an AI/ML model, this question is not relevant.

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XN CAL is used for the calibration and calibration verification of Sysmex XN series (XN-10, XN-20) analyzers. Assayed parameters include:

WBC (103/μL), RBC (108/μL), HGB (g/dL), HCT (%), PLT (103/ μL), and RET (%)

XN CAL™ is an in-vitro diagnostic product that contains the following: stabilized red blood cell component(s), stabilized white blood cell component(s), stabilized platelet component(s), and stabilized nucleated red blood cell component(s) in a preservative medium. The product is packaged in polypropylene plastic vials with screw caps containing 3 ml. The vials will be packaged in (5) welled or (1) welled vacuum formed clamshell container with the Instructions for Use (IFU) / assay sheet. The product must be stored at 2 - 8° C.

Here's an analysis of the provided text regarding the acceptance criteria and study for the XN CAL™ device, formatted as requested:

Acceptance Criteria and Device Performance Study

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state quantitative acceptance criteria in a table format, nor does it provide specific numerical performance results for the XN CAL™ device against such criteria. Instead, it makes general claims about the device's performance relative to the predicate. The "Discussion of Tests and Test Results" section indicates that the studies demonstrated that XN CAL™ is "consistently reproducible, substantially equivalent to the predicate product and stable for the shelf life claimed."

Based on the text, the implicit acceptance criteria would be that the device:

• Is consistently reproducible. (No specific numerical reproducibility target given.)
• Is substantially equivalent to the predicate product (X-CAL™). (No specific equivalence margin or statistical criteria are provided.)
• Is stable for the claimed shelf life. This includes:
  • Open-Vial Stability: 4 hours (Claim based on "Comparison to Predicate Device" table).
  • Closed-Vial Stability: 35 days (Claim based on "Comparison to Predicate Device" table).

The reported device performance, in the absence of numerical data, is a qualitative statement: "All testing showed that XN CAL™ is consistently reproducible, substantially equivalent to the predicate product and stable for the shelf life claimed."

Table of Implicit Acceptance Criteria and Reported Performance:

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

The 510(k) summary does not specify the sample size used for the test set in the Open-Vial Stability, Closed-Vial Stability, or Precision Performance studies.

The data provenance is not explicitly stated (e.g., country of origin, retrospective/prospective). As the submitter is Streck, a US-based company, and the context is a US regulatory filing, it is highly probable the data was generated in the US or under US guidance, but this is not explicitly confirmed. The studies are described in the past tense ("were conducted"), suggesting they were completed prior to submission, but does not explicitly state if they were retrospective or prospective.

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

This information is not applicable and not provided in the document. XN CAL™ is a quality control material (calibrator) for hematology analyzers. The "ground truth" for calibrator performance is established through laboratory methods and reference standards, not by expert interpretation of patient data as might be the case for diagnostic imaging or AI devices. The parameters (WBC, RBC, HGB, HCT, PLT, RET) are quantitative measurements.

4. Adjudication Method for the Test Set

This information is not applicable and not provided. Adjudication typically refers to resolving disagreements among multiple human readers/experts. Since the "ground truth" for a calibrator is based on instrumental measurements and adherence to specifications, not expert consensus on qualitative interpretation, an adjudication method is not relevant here.

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

A MRMC comparative effectiveness study was not done. This type of study is relevant for AI-assisted diagnostic devices where human readers interpret medical cases. XN CAL™ is a calibrator, not an AI diagnostic device. Therefore, AI assistance for human readers is not a component of this device's intended use.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

A standalone performance study was not done/is not applicable in the context of an algorithm. XN CAL™ is a physical chemical product (an in-vitro diagnostic product), not a software algorithm. Its performance is evaluated through laboratory stability and precision testing on hematology analyzers, not through an algorithm's standalone accuracy.

7. The Type of Ground Truth Used

The "ground truth" for performance of XN CAL™ would be established through a combination of:

• Reference Methods/Standard Measurements: The concentrations/values of the assayed parameters (WBC, RBC, HGB, etc.) in the calibrator material would be determined using highly accurate and precise reference methods or against established reference materials.
• Predicate Device Performance: A key aspect of the "ground truth" in this 510(k) is the performance of the legally marketed predicate device (X-CAL™), as the newer device (XN CAL™) aims to demonstrate substantial equivalence.

8. The Sample Size for the Training Set

This information is not applicable and not provided. XN CAL™ is a physical calibrator 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 as there is no "training set" for this type of device.

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