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Carefusion NeutraClear™ Needle-free connector is a bidirectional valve allowing injections and infusions to be given or sample taken via an intravenous line; or samples taken via arterial line. It seals and protects the line when it is not activated. The Carefusion NeutraClear™ needle-free connector may be used with low-pressure power injectors procedures to a maximum pressure of 325psi with a flow at 10mL per second.

Carefusion NeutraClear™ Needle-free connector is a sterile, non-pyrogenic bidirectional needleless injection valve intended for single patient use as an accessory to intravascular administration sets allowing infusions to be given or samples taken via an intravenous line or via arterial line without using a needle, thus eliminating the potential for needle-stick injuries during use. It seals and protects the line when it is not activated. This bidirectional needleless injection valve may be used with low-pressure power injectors to a maximum pressure of 325psi with a flow at 10mL per second. The Carefusion NeutraClear™ can be used for up to 200 activations or 7 days.

The provided document is a 510(k) premarket notification letter and summary for a medical device: the Carefusion NeutraClear™ Needle-free Connector (EL-NC1000). The document describes the device, its indications for use, technological characteristics, and performance data used to demonstrate substantial equivalence to a predicate device (NeutraClear™ EL200).

However, the document does not contain the kind of detailed information about acceptance criteria and study design that would be typical for clinical studies of AI/ML devices, such as:

1. A table of acceptance criteria and reported device performance for specific metrics like sensitivity, specificity, or accuracy.
2. Sample sizes for test sets, data provenance, number/qualifications of experts, or adjudication methods for establishing ground truth in AI/ML performance evaluation.
3. Information on multi-reader multi-case (MRMC) comparative effectiveness studies or standalone algorithm performance.
4. Specifics about training set sample size or how ground truth was established for a training set (as this is not an AI/ML device).

Instead, this document describes the evaluation of a physical medical device (a needle-free connector) through non-clinical performance testing and adherence to recognized standards. The "acceptance criteria" here refer to the successful completion and meeting of requirements outlined in these standards and internal test methods.

Therefore, I will extract the information available in the document regarding the device's performance evaluation, framing it within the context of a physical medical device rather than an AI/ML system.

Acceptance Criteria and Study for Carefusion NeutraClear™ Needle-free Connector (EL-NC1000)

The Carefusion NeutraClear™ Needle-free Connector (EL-NC1000) is a physical medical device, not an AI/ML device. Therefore, the "acceptance criteria" and "study" described in the document refer to non-clinical performance testing against recognized international standards and internal test methods, rather than clinical trials or AI/ML specific performance metrics like sensitivity/specificity derived from ground truth established by experts.

1. Table of Acceptance Criteria and Reported Device Performance

The document states that "The device met the acceptance criteria for all functional, microbial ingress, sterility, biocompatibility and other performance criteria, which verify it to be substantially equivalent to the predicate device." While specific numerical acceptance criteria (e.g., "leakage must be less than X mL/hr") are not explicitly listed in a table, the document refers to recognized standards and internal test methods that define these criteria.

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

This information is not explicitly provided in the document. For non-clinical performance testing of a physical device, sample sizes are typically determined by engineering principles, statistical power for specific tests (e.g., destructive testing, fatigue testing), and regulatory compliance testing requirements for the standards cited (e.g., number of units to test for sterility validation). The data provenance is internal laboratory testing.

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

Not applicable. Ground truth as typically understood in AI/ML (e.g., expert consensus on medical images) does not apply here. The "ground truth" for this device's performance is established by the objective physical and chemical properties of the device and its interaction with fluids and pressure, measured against established engineering and biological safety standards.

4. Adjudication Method for the Test Set

Not applicable, as there is no "ground truth" derived from human interpretation. Performance is assessed through objective measurements and validated test methods.

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

Not applicable. This is a physical medical device, not an AI/ML system requiring human-in-the-loop performance evaluation.

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

Not applicable. This is a physical medical device, not an AI/ML system. The "standalone" performance here refers to the device's ability to meet its functional and safety requirements independently, as demonstrated by the non-clinical tests.

7. Type of Ground Truth Used

The "ground truth" for the performance of this physical device is defined by:

• Established engineering principles: Performance under pressure, flow rates, leak resistance, etc.
• Biological safety standards: Biocompatibility, sterility, absence of hemolysis.
• Regulatory standards: Conformity to ISO standards and FDA requirements for medical devices.
• Internal specifications: Defined by the manufacturer for functional parameters.

8. Sample Size for the Training Set

Not applicable. This is a physical medical device, not an AI/ML model that requires a training set.

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

Not applicable. This is a physical medical device, not an AI/ML model.

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