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Calibrator for automated systems (C.f.a.s) is for use in the calibration of quantitative Roche methods on Roche clinical chemistry analyzers as specified in the value sheets.

C.f.a.s. (Calibrator for automated systems) CK-MB is for use in the calibration of Roche methods for the quantitative determination of CK-MB on Roche clinical chemistry analyzers as specified in the value sheets.

1. C.f.a.s. is for use in the calibration for automated Roche methods on Roche chemistry analyzers as specified in the enclosed value sheet. It is a lyophilized calibrator based on human serum.
2. C.f.a.s. CK-MB is for use in the calibration of Roche methods for the quantitative determination of the MB Isoenzyme of Creatine kinase on automated clinical chemistry analyzers. It is a lyophilized calibrator based on bovine serum albumin.

The provided text describes modifications to two calibrator devices, Calibrator for automated systems (C.f.a.s.) and C.f.a.s. CK-MB, and establishes substantial equivalence to predicate devices. However, it does not contain information about specific acceptance criteria or a study proving the device meets those criteria, as typically found in a performance study report for diagnostic devices. The document is a 510(k) Summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed performance study with acceptance criteria.

Therefore, many of the requested fields cannot be filled from the provided text.

Here's a breakdown of what can be extracted and what cannot:

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

The document does not specify quantitative acceptance criteria (e.g., accuracy, precision targets) for the modified calibrators or report specific device performance against such criteria. It primarily focuses on comparing features and source materials to demonstrate substantial equivalence to previously cleared devices.

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 provided in the 510(k) Summary. Performance data (if collected) would typically be in a separate study report, which is not part of this summary.

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 relevant to a calibrator device and is not provided. Ground truth for calibrators is typically established through reference methods and reference materials, not expert consensus as in image-based diagnostics.

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

This information is not relevant to a calibrator device and is not provided.

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 type of study is not relevant to a calibrator device and was not conducted. The devices are calibrators for automated chemistry analyzers, not AI-assisted diagnostic tools.

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

This refers to AI algorithm performance studies, which are not relevant to these calibrator devices.

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

For calibrators, the "ground truth" or reference values are established through reference materials and reference methods, as noted in the "Traceability" section:

• C.f.a.s.: Uses Reference Materials like SRM 909b, SRM 956, SRM 929, SRM 914, SRM 967, SRM 1951, ERM DA470k, and USP Primary Reference Material. It also references updated IFCC Manuals for various enzymes as reference method publications.
• C.f.a.s. CK-MB: The traceability is described as being "described in the reagent system instructions for use," and value assignment is performed by running new lots against previously assigned lots and verifying with reference material, Master lot C.f.a.s. CK-MB, and previously assigned lots.

8. The sample size for the training set

This is not applicable as this is a calibrator device, not an AI algorithm that requires a training set.

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

This is not applicable for the same reason as point 8.

Summary of Provided Information:

The 510(k) Summary describes modifications to two calibrator devices (C.f.a.s. and C.f.a.s. CK-MB) primarily involving changes in the source materials for certain analytes. The submission aims to demonstrate substantial equivalence to previously cleared predicate devices (K062319 for C.f.a.s. and K003158 for C.f.a.s. CK-MB).

• C.f.a.s. modifications: Human recombinant Gamma-glutamyltransferase (γGT) replaces porcine kidney γGT; Human recombinant Aspartate aminotransferase (AST) replaces porcine heart AST.
• C.f.a.s. CK-MB modifications: Human recombinant Creatine Kinase Isoenzyme MB (CK-MB) replaces porcine brain Creatine Kinase Isoenzyme BB (CK-BB).

The document does not report specific acceptance criteria or a study demonstrating the modified devices meet new performance targets because the purpose of a 510(k) Summary for such device modifications is to show that the changes do not raise new questions of safety and effectiveness and that the modified device is substantially equivalent to an existing legally marketed device. The focus is on comparing features, intended use, format, stability, levels, reagent composition, traceability, value assignment, and source materials with the predicate devices.

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Calibrator for automated systems (C.f.a.s.) Proteins is for use in the calibration of quantitative Roche methods on Roche clinical chemistry analyzers as specified in the value sheets.

Calibrator for automated systems (C.f.a.s.) Proteins

The provided text is a 510(k) premarket notification letter from the FDA regarding a device named "Calibrator for automated systems (C.f.a.s.) Proteins." This document is a regulatory approval letter and does not contain the detailed information about acceptance criteria or a study that proves the device meets specific performance metrics.

Therefore, I cannot provide the requested information. The letter confirms that the FDA has reviewed the submission and determined that the device is substantially equivalent to legally marketed predicate devices, allowing it to be marketed. It does not include the results of performance studies, sample sizes, expert qualifications, or ground truth establishment.

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Calibrator for Automated Systems (C.f.a.s.) is for use in the calibration of quantitative Roche methods on Roche chemistry analyzers as specified in the enclosed value sheet.

Calibrator for Automated Systems (C.f.a.s.) is for use in the calibration of quantitative Roche methods on Roche chemistry analyzers as specified in the enclosed value sheet. Calibrator for Automated Systems (C.f.a.s.) is a lyophilized calibrator based on human serum.

This 510(k) submission describes modifications to an existing calibrator device, the Calibrator for Automated Systems (C.f.a.s.). It focuses on demonstrating substantial equivalence to the predicate device (K033501) rather than a comprehensive de novo study with acceptance criteria and a detailed study report comparing the device's performance against those criteria. Therefore, much of the requested information regarding an acceptance criteria study in the typical sense (e.g., sample size, ground truth, expert opinions, MRMC studies, standalone performance) is not directly present.

However, based on the provided text, we can infer some "acceptance criteria" through the modifications and the declaration of conformity to design controls, and describe the "study" as the validation activities conducted under design control.

Here's an attempt to address your request based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a modification submission, the "acceptance criteria" are implied by demonstrating that the modified device's performance, particularly its stability and traceability, remains appropriate for its intended use and is substantially equivalent to the predicate. The "reported device performance" is the successful outcome of the validation and development activities.

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

The document does not specify a "test set" in the context of a clinical or analytical study with a distinct sample size for this 510(k) modification. The validation and development activities would have involved various measurements and comparisons, but specific sample sizes for these internal studies are not detailed.

• Data Provenance: The studies were conducted internally by Roche Diagnostics Corporation, likely at their Indianapolis, IN (USA) facility. The nature of the data would be laboratory-generated analytical data from the calibrator. The studies are prospective in the sense that they were conducted for this device modification.

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

This type of information is generally not applicable to the analytical performance testing of a calibrator. "Ground truth" for a calibrator is established through highly controlled, traceable analytical methods and reference materials, not typically through a panel of human experts. The data supporting the performance reflects the analytical measurements themselves.

4. Adjudication Method for the Test Set

Not applicable, as human expert adjudication is not relevant for establishing the performance of an analytical calibrator.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

Not applicable. This device is an analytical calibrator, not an imaging device or diagnostic tool that requires human interpretation. Therefore, MRMC studies are not relevant.

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

This concept is not directly applicable to a chemical calibrator. The calibrator itself is a "standalone" product that performs its function (calibration) without human "interpretation" of its result in the way an algorithm might. Its performance is measured analytically.

7. The Type of Ground Truth Used

The ground truth for a calibrator’s performance (e.g., the accuracy of its assigned values and its stability) is established through:

• Reference materials: Highly characterized substances with known, accurate concentrations or properties.
• Reference methods: Established, validated analytical procedures known to provide accurate results.
• Traceability: The ability to link the measurements to national or international standards.

The document explicitly states "Traceability through standards or reference methods analyzed at all set point validation runs" and "Values are verified by using reference material, Master lot C.f.a.s. and previously assigned lots of C.f.a.s." (Paragraph 2, Table: Similarities/Differences).

8. The Sample Size for the Training Set

Not applicable. This device is a calibrator, not an AI/ML algorithm that requires a "training set."

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

Not applicable, as there is no "training set."

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C.f.a.s. (Calibrator for automated systems) HbA1c is for use in the calibration of quantitative Roche methods on Roche clinical chemistry analyzers as specified in the enclosed value sheet.

C.f.a.s. HbA1c is single-level lyophilized calibrator based on hemolyzed sheep blood and human blood. The concentrations of the calibrator components have been adjusted to ensure optimal calibration of the appropriate Roche methods on clinical chemistry analyzers. During use, calibrator dilutions are prepared automatically on-board the analyzer resulting in six levels.

The provided text is a 510(k) summary for a medical device called "C.f.a.s. (Calibrator for Automated Systems) HbA1c". This document focuses on demonstrating substantial equivalence to a predicate device, rather than providing a detailed study report with specific acceptance criteria and performance data as often seen for diagnostic or therapeutic devices.

Therefore, many of the requested elements for a study proving device performance are not directly available in the provided text. The summary emphasizes the similarities and differences between the new calibrator and its predicate, asserting that these differences do not raise new questions of safety or effectiveness.

Here's a breakdown of what can and cannot be extracted from the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not explicitly stated in the document as it would be for a typical performance study. For a calibrator, "performance" is typically related to its stability, accuracy in providing a known concentration, and its ability to enable accurate measurements by the instrument it calibrates. The document asserts that the new device is substantially equivalent to a predicate calibrator.

The "acceptance criteria" for a calibrator like this would typically involve:

• Assigned value accuracy: The calibrator, once reconstituted, must have an accurate and stable assigned value for HbA1c.
• Stability: Meeting the stated stability claims (unopened, reconstituted at different temperatures and durations).
• Method compatibility: Successfully calibrating Roche clinical chemistry analyzers for Roche HbA1c methods, leading to accurate patient sample results.

The document implies these criteria are met by stating substantial equivalence to a predicate device with identical core characteristics. No quantitative performance metrics are provided in this summary.

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

This information is not provided in the document. As a 510(k) summary for a calibrator, it focuses on the device's characteristics and its equivalence to a predicate, not on a clinical trial with a "test set" of patient data.

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

This information is not applicable/not provided. The device is a calibrator, not a diagnostic tool that interprets patient data. Therefore, there's no "ground truth" for a test set of patient cases established by experts in this context. The "ground truth" for a calibrator would be its accurately assigned HbA1c concentration.

4. Adjudication Method for the Test Set

This information is not applicable/not provided. There is no "test set" of patient data that would require expert adjudication.

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

This information is not applicable/not provided. This type of study is relevant for diagnostic imaging or interpretation devices where human performance is being evaluated, and is not relevant for a calibrator.

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

This information is not applicable/not provided. The device is a calibrator, not an algorithm.

7. The Type of Ground Truth Used

For a calibrator, the "ground truth" would be the assigned value of the analytes (HbA1c in this case) at each level of the calibrator. This value is established through highly accurate reference methods and/or traceability to international reference materials. The document implies this "ground truth" is reliably established, making the calibrator suitable for its intended purpose.

8. The Sample Size for the Training Set

This information is not applicable/not provided. This concept applies to machine learning algorithms, not to a medical calibrator.

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

This information is not applicable/not provided.

In summary: The provided 510(k) summary for the C.f.a.s. HbA1c calibrator is a submission focused on demonstrating substantial equivalence to a predicate device. It highlights the physical, chemical, and functional similarities, and explains the differences (such as handling, levels, and stability conditions) without presenting specific, quantitative performance study data or details on "acceptance criteria" as they might apply to a diagnostic or therapeutic device. The "study" here is the comparison to the predicate device, asserting that the new device performs equivalently for its intended use as a calibrator.

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C.f.a.s. (Calibrator for automated systems) PUC (Proteins in Urine/CSF) is for use in the calibration of quantitative Roche methods on Roche clinical chemistry analyzers as specified in the enclosed value sheet.

The Calibrator for Automated Systems Proteins in Urine/CSF (C.f.a.s. PUC) consists of a buffered aqueous solution with biological materials added as required to obtain desired component levels. Values for constituent analytes are provided in product labeling.

The provided information does not contain any data regarding acceptance criteria or a study proving device performance in the context of an AI/ML medical device.

This document is a 510(k) summary for a Calibrator for Automated Systems Proteins in Urine/CSF (C.f.a.s. PUC), a biochemical calibrator. The summary focuses on demonstrating "substantial equivalence" to a predicate device, which is a regulatory pathway for certain medical devices that allows them to be marketed without undergoing the full premarket approval (PMA) process.

Here's why the requested information about acceptance criteria and a performance study for an AI/ML device is not present:

• Device Type: The device described, C.f.a.s. PUC, is a calibrator. Its function is to establish calibration curves for quantitative Roche methods on clinical chemistry analyzers. This is a chemical product used for laboratory testing, not an AI/ML algorithm or software for image analysis, diagnosis, or prediction.
• Regulatory Pathway: The 510(k) pathway for this type of device relies on demonstrating substantial equivalence to a previously legally marketed predicate device. This typically involves comparing physical characteristics, intended use, technological characteristics, and safety/effectiveness data if significant differences exist that could impact safety or effectiveness. For a calibrator, performance evaluation usually revolves around its ability to accurately calibrate the target assay, stability, and absence of interferences. It does not involve AI/ML performance metrics like sensitivity, specificity, AUC, or reader studies.
• Content of the Summary: The document details the submitter, device name, description, intended use, and a detailed comparison with a predicate device (C.f.a.s. Proteins). The comparison highlights similarities and differences, primarily in matrix composition (buffered aqueous solution vs. stabilized human serum) and constituent analytes. This is typical for a calibrator 510(k), focusing on the chemical and physical properties relevant to its function.
• Lack of AI/ML-specific terms: There is no mention of algorithms, machine learning, AI, training data, test sets, ground truth, expert readers, or any other terminology associated with AI/ML device validation.

Therefore, I cannot provide the requested information based on the given input. The input describes a different type of medical device and a different regulatory evaluation process.

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The C.f.a.s. PAC is intended for use in the calibration of quantitative Roche methods on Roche clinical chemistry analyzers as specified in the enclosed value sheet.

The C.f.a.s. PAC is a lyophilized product consisting of human serum with biological materials added as required to obtain desired component levels. Values for constituent analytes are provided in product labeling.

The provided document is a 510(k) summary for a medical device called "C.f.a.s. PAC," which is a calibrator for automated systems. It describes the device, its intended use, and its substantial equivalence to a predicate device. However, it does not contain the detailed study information regarding acceptance criteria, device performance, sample sizes, ground truth establishment, or expert involvement that you requested.

The document states under "Performance Characteristics":

• "The C.f.a.s. PAC was evaluated for value assignment and stability."

This phrase indicates that a study was performed, but the results, methodology, acceptance criteria, and other specifics of that study are not included in this summary. The 510(k) summary focuses on demonstrating substantial equivalence to a predicate device rather than providing a performance study report.

Therefore, I cannot fulfill your request to describe the acceptance criteria and the study that proves the device meets them based on the provided text. The requested information (Table of acceptance criteria and reported performance, sample sizes, data provenance, number and qualifications of experts, adjudication method, MRMC study, standalone performance, type of ground truth, training set sample size, and training ground truth establishment) is not present in this document.

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For use in the calibration of Roche lipid methods on automated clinical chemistry analyzers.

The Calibrator for Automated Systems (C.f.a.s.) consists of liquid human serum with biological materials added as required to obtain desired component levels. Values for constituent analytes are provided in product labeling.

The provided text describes a 510(k) summary for a medical device called "Calibrator for Automated Systems (C.f.a.s.) Lipids." This document focuses on establishing substantial equivalence to a predicate device, rather than providing a detailed study proving the device meets specific acceptance criteria in terms of performance metrics.

Here's an analysis based on the provided text, addressing your points where possible, and noting where information is not available:

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

The document does not explicitly state acceptance criteria in terms of numerical performance metrics (e.g., accuracy, precision, sensitivity, specificity) for the C.f.a.s. Lipids device itself. Instead, it focuses on demonstrating substantial equivalence to a predicate device, "Roche Diagnostic Calibrator for Automated Systems (C.f.a.s.) HDL/LDL-C plus (K974825)".

The "acceptance criteria" in this context are implicitly that the proposed device:

• Shares the same intended use as the predicate.
• Has similar features (format, stability, levels, matrix).
• Differences in features (constituent analytes) do not raise new questions of safety or effectiveness.

Table of Substantial Equivalence Comparison (from the provided text):

The "reported device performance" in this document is effectively the demonstration that the features and intended use are sufficiently similar to the predicate device, and where there are differences (e.g., additional analytes), these differences are acceptable for substantial equivalence. No specific numerical performance results (like accuracy percentages or coefficients of variation) for the new device are provided in this summary.

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 provided in the 510(k) summary. This type of summary focuses on the comparison of the device's characteristics with a predicate device, not on specific clinical or analytical studies that would involve test sets and data provenance. For a calibrator, studies typically involve testing its ability to consistently calibrate instruments across a range of values, but details of such studies are absent here.

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. This device is a calibrator, not a diagnostic tool requiring expert interpretation of images or patient data to establish ground truth.

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

This information is not applicable and not provided. As a calibrator, it doesn't involve subjective interpretations that would require 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. This device is a calibrator, not an AI-assisted diagnostic tool that would involve human readers or MRMC studies.

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

This information is not applicable and not provided. This device is a chemical calibrator; it does not involve an algorithm in the sense of AI or image processing.

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

For a calibrator, the "ground truth" would typically be derived from highly accurate reference methods or certified reference materials used to assign target values to the calibrator vials. The document states that "Values for constituent analytes are provided in product labeling," implying that these values are established and accepted as accurate. However, the method for establishing this "ground truth" (e.g., using a traceability chain to a primary reference method) is not detailed in this summary.

8. The sample size for the training set

This information is not applicable and not provided. As a calibrator, there isn't a "training set" in the context of machine learning or classification algorithms.

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

This information is not applicable and not provided for the same reasons as point 8.

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For use as a calibrator of clinical chemistry assays. Biological materials are added as required to obtain desired component levels. This calibrator material is well suited for automated analytical procedures. Levels of constituent analytes are provided in product labeling.

The Calibrator for Automated Systems (C.f.a.s.) consists of lyophilized human serum with biological materials added as required to obtain desired component levels. Values for constituent analytes are provided in product labeling.

The provided text is a 510(k) summary for a medical device called "Calibrator for Automated Systems (C.f.a.s.)". This document is a regulatory submission for a diagnostic calibrator, not an AI device or a device that directly interacts with human readers for diagnostic purposes. Therefore, many of the requested criteria, such as "acceptance criteria and reported device performance" in the context of clinical accuracy for an AI, "sample size for the test set," "number of experts used," "adjudication method," "MRMC study," and "standalone performance," are not applicable.

The document focuses on demonstrating substantial equivalence to a predicate device rather than presenting performance data against a specific set of acceptance criteria in the way one might for a new diagnostic test's accuracy.

However, based on the information provided, here's what can be extracted and what cannot:

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

This type of comprehensive table with quantitative acceptance criteria and corresponding reported device performance with metrics like sensitivity, specificity, accuracy, etc., is not provided in this 510(k) summary. The summary focuses on comparing characteristics and intended use to a predicate device to establish substantial equivalence.

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

• Sample size for test set: Not applicable and not provided. The device is a calibrator, and its "performance" is assessed through its characteristics and intended use, not clinical test set performance in a diagnostic sense.
• Data provenance: Not applicable.

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

Not applicable. Ground truth in the context of diagnostic accuracy for a clinical test is not established for a calibrator in this manner. The "truth" for a calibrator refers to the known concentrations of analytes, which are established through analytical methods, not expert consensus.

4. Adjudication method for the test set:

Not applicable.

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 a calibrator, not an AI or a device designed to assist human readers in image interpretation or diagnosis.

6. 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.

7. The type of ground truth used:

For a calibrator, the "ground truth" would be the analyte concentrations established through reference methods and internal quality control procedures during the manufacturing and characterization of the calibrator material. The document states: "Values for constituent analytes are provided in product labeling." This implies that the manufacturer determines these values.

8. The sample size for the training set:

Not applicable. This is a physical calibrator product, not a machine learning model that requires a training set.

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

Not applicable.

Summary of what is applicable from the provided text regarding "acceptance criteria" (understood as characteristics deemed acceptable for substantial equivalence):

The "acceptance criteria" here are implicitly tied to the characteristics of the predicate device for demonstrating substantial equivalence. The study implicitly proving these "criteria" are met is the comparison to the predicate device itself.

Study Proving Device Meets "Acceptance Criteria" (Substantial Equivalence):

The "study" is the comparison to the predicate device, Roche Serum Calibrator, as outlined in the 510(k) submission. The FDA reviewed this comparison and determined that the C.f.a.s. is substantially equivalent. This means that, based on the characteristics listed, the new device is considered as safe and effective as the predicate device. The detailed analytical methods used to characterize the analyte values and stability of both the predicate and proposed calibrator are not detailed in this summary but would be part of the full submission that the FDA reviewed.

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