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Hospira Primary sets are indicated for the delivery of fluids from a container to a patient's vascular system.

The Hospira Primary Sets with Secure Lock are intended for use as gravity sets. Hospira infusion sets are disposable devices for single patient use.

This document is a 510(k) premarket notification for a medical device called "Hospira Primary Sets." It describes the device, its intended use, and claims substantial equivalence to previously marketed predicate devices.

Based on the provided text, the device is an Intravascular Administration Set, which is a hardware device, not an AI/ML device.

Therefore, I cannot provide information regarding acceptance criteria and studies related to AI/ML device performance, as the requested information is not applicable to this submission. The document discusses performance testing in a general sense to ensure the device performs as intended in accordance with applicable standards, but does not provide specific acceptance criteria or study details that would be relevant to an AI/ML device.

Here's an analysis based on the document if we were to interpret it for a standard medical device, but with the caveat that it doesn't involve AI/ML:

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

The document broadly states: "Performance testing was conducted to ensure the device performs as intended in accordance with applicable standards. All testing is acceptable."

However, it does not provide a table of specific acceptance criteria (e.g., flow rate, burst pressure, leak rate maximums) or their corresponding reported device performance values. It only states that the product Sterility Assurance Level is 10-6, which is a performance characteristic, but not part of a comparative table.

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

The document does not specify the sample size used for performance testing, nor does it mention the data provenance (country of origin, retrospective/prospective). It only refers to "performance testing" generally.

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 as the device is a hardware product (intravascular administration set) and does not involve the interpretation of data by experts to establish ground truth in the way an AI/ML device would.

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

This information is not applicable for the same reason as point 3. It's a hardware device, not an AI/ML diagnostic tool.

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 as the device is a hardware product and does not involve human readers or AI assistance.

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

This information is not applicable as the device is a hardware product and does not have an algorithm.

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

For a hardware device like this, "ground truth" would typically refer to established engineering and material standards, or physical measurements that confirm the device meets its specifications (e.g., fluid flow rate, material strength, sterility). The document states: "All materials of construction for Hospira Primary Sets meet the applicable material test requirements for ISO 10993." and "Performance testing was conducted to ensure the device performs as intended in accordance with applicable standards." This implies that industry standards and physical measurements are used as the "ground truth" for evaluating the device's performance.

8. The sample size for the training set:

This information is not applicable as the device is a hardware product and does not involve machine learning or training sets.

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

This information is not applicable as the device is a hardware product and does not involve machine learning or training sets.

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