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The device is used for the administration of fluids from a container into the patient's vascular system through a vascular access device. The intended populations include adults and pediatrics except neonates.

The proposed device is a single used device. It includes 30 models. For the 30 different models, each configuration comprises of various components which may include: Male luer lock and protector, Female luer lock and protector. Tube, Flow regulator, Clamp, multiple ways connector, Precision Liquid filter, Needle free connector, Check valve, Graduated flow regulator. Injection site and stopcock. The detail description of models is provided in Table 1.

The proposed devices are sterilized by EO to achieve a SAL 106 and supplied in sterility maintenance package which could maintain the sterility of the device during the shelf life of 5 years.

The provided text is a 510(k) summary for a medical device (Disposable Extension Set) seeking FDA clearance. It focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing. This document does not include information about AI/ML algorithm performance, comparative effectiveness studies, or the types of ground truth typically associated with such studies.

Therefore, many of your requested items cannot be extracted from this document, as they pertain to studies on intelligent algorithms rather than a physical medical device like an extension set.

However, I can provide the available information:

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

The document doesn't explicitly state "acceptance criteria" for each test in a numerical format. Instead, it states that the device "complies with the following standards" or that "test results demonstrated that the proposed device complies with the following standards." The "reported device performance" is implicitly that it met the requirements of these standards.

Regarding the other points, as per medical device clearance context:

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

  • This document does not specify sample sizes for the performance tests as it's a summary of a physical device's compliance with standards, not an AI/ML study.
  • Data provenance (country of origin, retrospective/prospective) is not applicable or specified for these types of engineering/material tests.

• 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):

  • Not applicable. "Ground truth" in the context of an AI/ML study does not apply to the regulatory clearance of a physical medical device like an extension set.

• 4. Adjudication method (e.g. 2+1, 3+1, none) 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 is not an AI-assisted device.

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

  • Not applicable. This is a physical medical device.

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

  • Not applicable. The "ground truth" for this device is its adherence to established engineering and biological safety standards (e.g., seal strength, biocompatibility, flow rates) through laboratory testing.

• 8. The sample size for the training set:

  • Not applicable. There is no AI/ML component; therefore, no "training set."

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

  • Not applicable.

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