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510(k) Data Aggregation
(71 days)
The C3 Flex® reagent cartridge for the Dimension® clinical chemistry system is an in vitro diagnostic test intended to quantitatively measure complement C3 (C3) in serum as an aid in the diagnosis of immunologic disorders associated with complement C3 protein.
The C3 Flex® reagent cartridge for the Dimension® clinical chemistry system is a quantitative, turbidimetric assay using endpoint detection, based on the precipitation of complement CJ by its polyclonal antibody.
Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria for the C3 Flex® Reagent Cartridge are implicitly defined by its substantial equivalence to the predicate device, the Beckman Array® Complement C3 Assay. The key performance metric used to demonstrate this equivalence is the correlation between the two methods when testing patient samples.
Acceptance Criteria (Implied) | Reported Device Performance | Met? |
---|---|---|
Correlation coefficient (r) indicative of strong agreement with predicate device | 0.980 | Yes |
Slope of regression analysis close to 1.00 | 1.01 | Yes |
Intercept of regression analysis close to 0 | 6.7 mg/dL | Yes (within reasonable range for clinical assays) |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: 94 clinical patient samples.
- Data Provenance: The text does not explicitly state the country of origin. The samples are referred to as "clinical patient samples," implying they were collected from human patients and are likely retrospective.
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 study relies on a comparative method (split sample comparison) where the predicate device's results are considered the reference, rather than independent expert-established ground truth.
4. Adjudication Method for the Test Set
This information is not provided in the document, as the ground truth was established by comparison to a predicate device rather than 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
A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This device is an in vitro diagnostic (IVD) reagent cartridge for a clinical chemistry system, not an AI-powered diagnostic imaging or decision support system that involves human readers.
6. If a Standalone (i.e. algorithm only without human-in-the loop performance) was done
This refers to the performance of the device itself (the C3 Flex® reagent cartridge on the Dimension® system), which is intrinsically "standalone" in the context of an IVD assay. The performance data presented (correlation, slope, intercept) represents the standalone performance of the device compared to the predicate.
7. The Type of Ground Truth Used
The "ground truth" for the test set was established by comparison to a legally marketed predicate device (Beckman Array® Complement C3 Assay). The results from the predicate device served as the reference standard against which the new device was evaluated.
8. The Sample Size for the Training Set
The document does not explicitly mention a training set in the context of this 510(k) submission. This is common for traditional IVD assays like a reagent cartridge, where development involves optimization and validation rather than machine learning model training based on a distinct dataset. The "94 clinical patient samples" were used for the comparative testing of the device's performance against the predicate.
9. How the Ground Truth for the Training Set was Established
As no training set is explicitly mentioned or defined in the context of machine learning, this question is not applicable based on the provided text. The development process for such a device would likely involve internal validation and optimization against reference materials and known samples, but not "ground truth" derived from expert consensus or pathology on a distinct training set in the way AI models are trained.
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