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For the quantitative determination of cortisol in serum using the Ciba Corning Automated Chemiluminescence Systems.

The Ciba Corning ACS Cortisol assay is a competitive chemiluminescent immunoassay. Cortisol in the patient sample competes with the acridinium ester (AE-labeled cortisol (Lite Reagent) for binding to polyclonal rabbit anti-cortisol antibody on the Solid Phase. The polyclonal rabbit anti-cortisol antibody is bound to monoclonal mouse anti-rabbit antibody, which is coupled to paramagnetic particles (solid phase). An inverse relationship exists between the amount of cortisol present in the patient sample and the amount of relative light units (RLUs) detected by the ACS:180® system.

The provided text describes a medical device, the Ciba Corning ACS Cortisol Immunoassay, and its performance data as part of a 510(k) summary. However, this document does not contain any information about acceptance criteria, clinical studies with human readers, or an AI device.

The document describes an immunoassay, which is a laboratory test that measures the presence or concentration of a substance (in this case, cortisol) using the reaction of an antibody to its antigen. This is a chemical/biological assay, not an AI or imaging device.

Therefore, I cannot provide the requested information, such as:

• A table of acceptance criteria and the reported device performance: This document reports sensitivity and accuracy against predicate devices, but it doesn't define "acceptance criteria" in the context of an AI-driven device with performance metrics like specificity, sensitivity, or AUC.
• Sample size used for the test set and the data provenance: While sample sizes for accuracy studies are given (100 extracted urine samples, 70 extracted urine samples), there's no mention of a "test set" in the context of AI. Data provenance is not specified beyond "patient sample" or "extracted urine samples."
• Number of experts used to establish the ground truth... and qualifications: Not applicable as this is not an AI/imaging device requiring expert interpretation for ground truth.
• Adjudication method: Not applicable.
• Multi reader multi case (MRMC) comparative effectiveness study: Not applicable, as there are no human readers or AI in this context.
• Standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable.
• Type of ground truth used: For the accuracy section, the "ground truth" seems to be the results obtained by the predicate devices (Fluorescence Polarization Immunoassay and Radioimmunoassay).
• Sample size for the training set: Not applicable, as there is no AI model to train.
• How the ground truth for the training set was established: Not applicable.

In summary, the provided document describes a traditional in-vitro diagnostic immunoassay. It does not contain any information related to AI, machine learning, or clinical studies involving human readers or expert consensus for ground truth, which are the core components of your request.

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The intended use of Ciba Corning Ciba Corning ACS:180 DPD Assay is for the quantitative of deoxypyridinoline (DPD) in urine using the Ciba Corning ACS:180 automated chemiluminescence systems

The Ciba Corning ACS:180 DPD assay is a competitive immunoassay using direct, competitive chemiluminescent technology. DPD in the patient sample competes with pvridinoline bound to the paramagnetic particles in the Solid Phase for a limited amount of monoclonal mouse anti-DPD antibody in the Lite Reagent. The monoclonal mouse anti-DPD is bound to goat antibody labeled with acridinium ester. An inverse relationship exists between the amount of DPD present in the sample and the amount of relative light units (RLUs) detected by the system.

This document describes the Ciba Corning ACS:180 DPD Assay, an in-vitro diagnostic device for measuring deoxypyridinoline (DPD) in urine. As a diagnostic device, the acceptance criteria and performance are related to analytical characteristics rather than diagnostic accuracy as would be found in imaging AI. Therefore, I will adapt the requested sections to align with the provided information.

1. Table of Acceptance Criteria and Reported Device Performance

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

For the method comparison study:

• Sample Size: 752 urine samples.
• Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). However, given it's a 510(k) submission from a US company for a new diagnostic device, it typically implies data collected in a clinical lab setting, likely prospective or from stored biobank samples.

For the sensitivity and precision studies:

• Sample Size (Sensitivity): 20 replicate determinations of the DPD zero standard.
• Sample Size (Precision): Not explicitly stated, but typically involves multiple replicates across different concentrations and runs.
• Data Provenance: Not explicitly stated.

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

This is not applicable to this type of in-vitro diagnostic device study. Ground truth is established by the results of the reference (alternate) method and the physical properties of the assay, not expert interpretation of images or patient data.

4. Adjudication Method for the Test Set

This is not applicable to this type of in-vitro diagnostic device study. Adjudication typically refers to resolving discrepancies between multiple human readers or between human readers and an AI; this study compares analytical results between two assays.

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 is not applicable. The device is a laboratory assay, not an AI for human interpretation or decision support. No human readers are involved in the performance of the assay itself.

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

Yes, the performance characteristics (sensitivity, method comparison, precision) described are for the Ciba Corning ACS:180 DPD Assay standalone performance. It is an automated chemiluminescence system, operating independently to produce DPD concentration values.

7. The Type of Ground Truth Used

• Sensitivity: The ground truth for determining the minimum detectable concentration is implicitly the "zero standard" (a sample with no DPD) and statistical analysis (two standard deviations less than the mean RLUs).
• Method Comparison: The "ground truth" or reference standard for comparison was an "alternate ELISA method" (Pyrilinks®-D, the predicate device), as the study aims to show correlation and agreement between the new assay and an established method.
• Precision: The ground truth for precision is the statistical variability of the assay's own measurements when performed repeatedly.

8. The Sample Size for the Training Set

This is not applicable in the context of traditional in-vitro diagnostic assays, especially those using competitive immunoassay technology. These devices are developed and optimized through chemical and biochemical engineering, not through machine learning training sets. Therefore, there is no "training set" in the AI sense.

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

As there is no "training set" for this type of device, this question is not applicable. The development and optimization of the assay would involve experimental validation steps to ensure robust and accurate chemical reactions and detection, rather than establishing ground truth for a training dataset.

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The intended use Certain® Hct is as a bi-level quality control material and CVM™ is a calibration verification material both for use with hematocrit analyzers.

The Chiron Certain Hct is a bi-level quality control material uniquely formulated to provide quality control values for hematocrit analyzers. There are two levels of Certain Hct each provided in 2.5 mL ampules. The product allows verfication of analyzer hematocrit performance within established ranges.

Chiron Calibration Vefication Material (CVM™) is formulated to verify the calibration of hematocrit analyzers. The product contains four levels of 2.5 mL ampules and is intended to assist laboratories in compliance with the requirements of calibration verification under CLIA.

This looks like a 510(k) premarket notification for a hematocrit quality control and calibration verification material, not a medical device in the typical sense of a diagnostic or therapeutic tool that relies on complex algorithms or human interpretation of images/data.

Therefore, many of the requested categories in your prompt (such as "number of experts," "adjudication method," "MRMC study," "standalone performance," "training set size") are not applicable to this type of product. The focus for this submission would be on the analytical performance of the control material itself, often demonstrated through precision, linearity, and stability studies.

Here's how I can address the applicable parts of your request based on the provided text:

Description of the Acceptance Criteria and Study:

The provided text K961807 is a "Summary of Safety and Effectiveness" for a quality control and calibration verification material for hematocrit analyzers. It describes the product (Certain® Hct and CVM™ Hct) and its intended use. However, the provided excerpt does not contain the specific acceptance criteria or the study details that demonstrate the device meets those criteria.

A typical 510(k) submission for a quality control material would include:

• Acceptance Criteria for Analytical Performance: This would likely involve specifications for parameters like:
  • Precision/Reproducibility: How consistently the material yields the same Hct value over repeated measurements. (e.g., %CV

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The Ciba Corning 400 System is intended for the point-of-care and laboratory testing of blood gases, electrolytes and metabolites in arterial, venous and capillary whole blood samples.

The 400 Series system analyzer is a point of care and laboratory testing analyzer used to for the direct measure of whole blood samples for the determination of the following parameters:

• partial pressures of carbon dioxide; pCO2
• partial pressure of oxygen pO2
• pH
• sodium; Na*
• potassium: K*
• ionized calcium; Ca**
• chloride: Cli
• alucose
• hematocrit: Hct

The provided text describes a medical device, the Ciba Corning 400 System, an analyzer for blood gases, electrolytes, and metabolites. It details the device's function, intended use, and technological characteristics. However, it does not contain the acceptance criteria or a study proving the device meets said criteria.

Therefore, I cannot provide the requested information, particularly:

• A table of acceptance criteria and reported device performance: This information is not present in the document.
• Sample sized used for the test set and the data provenance: There is no mention of a test set or its sample size or origin.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Ground truth establishment for a test set is not discussed.
• Adjudication method for the test set: No test set or adjudication is mentioned.
• 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 document predates widespread AI in medical devices and does not describe such a study.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No such study is mentioned.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc): Ground truth is not discussed in the context of device performance validation.
• The sample size for the training set: There is no mention of a training set as this is not an AI/ML device.
• How the ground truth for the training set was established: Not applicable, as there's no training set mentioned.

The document is a "Summary of Safety and Effectiveness" from 1996 for a 510(k) submission, focusing on describing the device, its predicate, and its measurement technology. It doesn't include the detailed performance study results and acceptance criteria typically found in more extensive validation reports.

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The Ciba Corning 348 System is intended for the point-of-care and laboratory testing of blood gases, electrolytes and metabolites in arterial, venous and capillary whole blood samples.

The 348 Series system analyzer is a point of care and laboratory testing analyzer used for the direct measure of whole blood samples for the determination of the following parameters:

• partial pressures of carbon dioxide; pCO2 .
• partial pressure of oxygen pO2 .
• pH
• sodium; Na* .
• potassium; K* ●
• ionized calcium; Ca** ●
• . hematocrit: Hct

Here's an analysis of the provided text, extracting the requested information about acceptance criteria and the study, while noting where information is not present:

The provided text describes a medical device, the Ciba Corning 348 System, and its technological characteristics. However, it does not contain information about acceptance criteria for device performance, nor does it detail a specific study proving the device meets such criteria.

The document is a 510(k) Summary, which typically focuses on demonstrating substantial equivalence to a predicate device rather than presenting detailed performance study results against predefined acceptance criteria. While it mentions the device measures various analytes, it doesn't quantify expected accuracy or precision.

Therefore, most of the requested fields cannot be filled from the given input.

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

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

• Cannot be answered from the provided text. The document lists the analytes the device measures (pCO2, pO2, pH, Na+, K+, Ca++, Hct) and their measurement technologies, but it does not specify any acceptance criteria (e.g., accuracy, precision limits) or report specific performance metrics against such criteria.

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

• Cannot be answered from the provided text. No information about a test set sample size, data provenance, or study design (retrospective/prospective) is available.

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)

• Cannot be answered from the provided text. There is no mention of experts or a ground truth establishment process for any test set.

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

• Cannot be answered from the provided text. No information about an adjudication method is 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

• Cannot be answered from the provided text. This device is an analyzer for blood gases, electrolytes, and metabolites, not an imaging or AI-assisted diagnostic tool. Therefore, an MRMC study or AI assistance is not applicable to this type of device.

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

• Cannot be answered from the provided text. While the device operates automatically, the document doesn't detail performance studies, standalone or otherwise. This question is more typically relevant to AI/algorithm-based devices, which this is not.

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

• Cannot be answered from the provided text. No information about ground truth is mentioned. For this type of device, ground truth would typically come from a reference method or validated laboratory standard.

8. The sample size for the training set

• Cannot be answered from the provided text. This device isn't described as having a training set in the context of machine learning. The technology relies on established electrochemical principles, not a data-driven training paradigm in the modern AI sense.

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

• Cannot be answered from the provided text. See explanation for point 8.

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The intended use of ACS LH2 is for the quantitative determination of LH in serum using the Ciba Corning Automated Chemiluminescence Systems.

The Ciba Corning ASC LH2 assay is a two-site chemilumometric (sandwhich) assay which uses constant amounts of two antibodies that have specificity for the intact LH molecule. The first antibody or Lite Reagent is a monoclonal mouse anti-LH antibody labeled with acridinium ester. The second antibody or solid phase is a monoclonal mouse anti-LH antibody covalently coupled to paramagnetic particles. A direct relationship exists between the LH in a sample and the relative light units (RLUs) detected by the ACS:180 systems.

The provided text describes a medical device called the ACS LH2 Immunoassay but does not contain information typically associated with acceptance criteria and study designs for AI-powered devices or complex imaging analysis. Instead, it focuses on the analytical performance characteristics of a laboratory immunoassay.

Therefore, many of the requested categories for AI/ML device studies cannot be answered directly from the provided text.

Here's an attempt to extract and interpret the available information in the context of the questions asked, acknowledging the limitations:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: 689 samples were used for the accuracy assessment against a reference method.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). Since this is a lab assay, samples would likely be from clinical specimens.

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

• This question is not applicable in the context of this immunoassay. The "ground truth" for an immunoassay is typically established by a reference method assay, which is itself a laboratory test, not human expert consensus on an image or clinical observation.

4. Adjudication method for the test set

• Not applicable. The "ground truth" is a measurement from a reference assay, not a subjective interpretation requiring 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

• No. This is an automated immunoassay, not an AI-powered diagnostic imaging or decision support tool that uses human readers.

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

• Yes, in a sense. The performance data (sensitivity, accuracy, precision) describes the standalone performance of the ACS LH2 immunoassay system without human interpretation affecting the measurement itself. A human will interpret the final numerical result, but the device's performance characteristics are measured intrinsically.

7. The type of ground truth used

• Reference Method Assay: The accuracy was determined by comparing the ACS LH2 assay results to a "reference method assay" for LH. The specific nature of this reference method is not detailed but implies a well-established and validated laboratory procedure for LH quantification.

8. The sample size for the training set

• Not explicitly stated. For an immunoassay, "training set" doesn't strictly apply in the same way it does for machine learning models. Instead, the assay is developed and optimized using various reagents, calibrators, and internal validation samples. The 689 samples mentioned appear to be for validation/testing of the developed assay's performance rather than training a model.

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

• Not applicable in the AI/ML context. For an immunoassay, development and optimization involve extensive analytical testing to ensure reagent stability, calibration linearity, matrix effects, etc., which is a different process than establishing "ground truth" for a training set in AI/ML.

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The intended use of ACS CKMB II is for the quantitative determination of CK-MB (CK-2) in serum or plasma using the Ciba Corning Automated Chemiluminescence Systems.

The Ciba Corning ACS CKMB II assay is a two-site chemilumometric (sandwhich) assay. which uses constant amounts of two antibodies. The first antibody or Lite Reagent is a monoclonal mouse anti-CK-MB antibody labeled with acridinium ester. The second antibody or solid phase is a monoclonal mouse anti-CK-BB antibody covalently coupled to paramagnetic particles. A direct relationship exists between the CK-MB in a sample and the relative light units (RLUs) detected by the ACS:180 systems. The assay is unaffected by increased levels of other CK isoenzymes.

The provided text describes a medical device, the "ACS CKMB II Immunoassay," and its performance but does not include information relevant to AI/ML device studies as outlined in your request.

Specifically, the document focuses on an immunoassay for detecting CK-MB, which is a laboratory test. The "performance data" section details traditional analytical performance characteristics of such an assay (Sensitivity, Accuracy using a correlation with a reference method, and Precision).

Therefore, I cannot extract the following information because it is not present in the provided text:

• A table of acceptance criteria and the reported device performance (in the context of AI/ML performance metrics like sensitivity, specificity, AUC, etc.): The document lists traditional analytical performance metrics.
• Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): While a sample size of 1744 is mentioned for the accuracy study, details on data provenance are absent.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable for an immunoassay.
• Adjudication method: Not applicable for an immunoassay.
• 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 is an immunoassay, not an imaging AI device.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: This is an immunoassay, not an AI algorithm.
• The type of ground truth used (expert concensus, pathology, outcomes data, etc): The ground truth for this immunoassay is likely the result from the "FEIA reference method."
• The sample size for the training set: Not applicable for an immunoassay, which does not typically involve machine learning training sets in this context.
• How the ground truth for the training set was established: Not applicable for an immunoassay.

In summary, the provided document describes a traditional in-vitro diagnostic device (an immunoassay) and its analytical performance, not an AI/ML powered device. Therefore, none of the requested AI/ML specific criteria can be extracted from this text.

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For the quantitative determination of free thyroxine (FT4) in serum using the Ciba Corning Automated Chemiluminescence Systems.

The Ciba Corning ASC FrT4 Immunoassay is a competitive immunoassay. FT4 in the-sample competes with the acridinium ester (AE-labeled T4 (Lite Reagent) for a limited amount of polyclonal rabbit anti-T4 antibody, which is covalently coupled to paramagnetic particles (solid phase). An indirect relationship exists between the FT4 in a sample and the relative light units (RLUs) detected.

Here's an analysis of the provided information, focusing on acceptance criteria and the study proving device performance:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document
does not explicitly state "acceptance criteria" but rather presents performance data. The table infers acceptance criteria based on the reported performance being considered acceptable for submission.

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

• Sample Size for Accuracy Test: 74 samples.
• Data Provenance: Not specified (country of origin, 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 provided in the document. The "ground truth" for the accuracy study appears to be a "RIA reference method assay," not expert adjudication.

4. Adjudication Method for the Test Set

This information is not applicable/not provided. The accuracy was assessed by comparing the device's results to an "RIA reference method assay," not through human 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

No, an MRMC comparative effectiveness study was not done. This device is an in-vitro diagnostic immunoassay, not an AI-assisted diagnostic imaging device that involves human readers.

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

Yes, a standalone performance evaluation was done. The entire "Performance Data" section describes the quantitative output of the device itself (sensitivity, accuracy against a reference method, and precision) without involving a human in the loop for interpreting the results in a diagnostic context.

7. The Type of Ground Truth Used

The ground truth used for the accuracy assessment was a reference method assay, specifically an "RIA reference method assay."

8. The Sample Size for the Training Set

This information is not provided in the document. The document describes studies to validate the performance of the device, not the training of a machine learning model.

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

This information is not applicable/not provided. The device described is an immunoassay, not a machine learning model that requires a training set and corresponding ground truth.

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Ask a specific question about this device