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0.9% Sodium Chloride Flush Syringes are intended for use in flushing compatible intravenous administration sets and indwelling intravenous access devices. Use according to the recommendations of the manufacture for the appropriate device.

The Predicate Device, 510(k) Number: K111034, AMUSA consists of a plastic syringe filled with 0.9% Sodium Chloride Flush Solution that is terminally sterilized. The predicate device filud path of 10 Section Onlonde Hush Oolution that is terminally sterlized. This is a single use device.

The Modified Device, the subject of this 510 (k), consists of a plastic syringe filled with 0.9% Sodium Chlerida Fluch Calification that the United on the Sodium Chloride Flush Solution that is terminally sterilized. The modified device fluid path sterile has a Sterility Assurance Level (SAL) of 108. This is a single use device.

The provided text describes a 510(k) submission for a medical device, the "0.9% Sodium Chloride Flush Syringe". However, it is not a study report for an AI/ML powered device, but rather a submission for a standard medical device that has undergone a change in component supplier.

Therefore, the information requested regarding acceptance criteria and studies for an AI/ML device (e.g., performance metrics like sensitivity/specificity, sample sizes for test/training sets, expert qualifications, MRMC studies, ground truth establishment) is not applicable to this document. The document focuses on demonstrating substantial equivalence to a predicate device for a physical product by verifying technical characteristics and conducting non-clinical testing.

Here's what can be extracted from the provided text, adapted to the context of a traditional medical device submission:

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

Since this is a Class II medical device being evaluated for substantial equivalence after a component supplier change, the acceptance criteria are not in terms of AI performance metrics, but rather in terms of demonstrating that the modified device is as safe and effective as the predicate device.

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

• Sample Size: The document does not specify exact sample sizes for the "chemical-physical, functional, and product stability testing." It only mentions that these tests were conducted.
• Data Provenance: Not explicitly stated, however, the testing would have been conducted by the manufacturer, AMUSA, or a qualified contract lab under their direction, as part of the regulatory submission process in the United States. It is "prospective" to the decision of equivalence, meaning it was performed specifically for this submission.

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

This concept is not applicable here. Ground truth in the context of this device relates to established engineering and quality standards for medical devices (e.g., sterility, material compatibility, functional performance). The "experts" would be the engineers, scientists, and quality assurance personnel performing and interpreting the non-clinical tests, and the FDA reviewers evaluating the submission. Their qualifications are assumed to be appropriate for their roles.

4. Adjudication method for the test set:

Not applicable in the context of ground truth for an AI/ML device. The "adjudication" for this type of device is the FDA's regulatory review process, where they evaluate the submitted data against regulatory requirements and accepted standards.

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 device is not an AI/ML product and does not involve human readers interpreting data with or without AI assistance.

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

Not applicable. This device is not an AI/ML algorithm.

7. The type of ground truth used:

The "ground truth" for this device is based on established engineering principles, material science, microbiology (for sterility), and functional performance standards relevant to sterile, single-use medical devices. It would involve objective measurements from the chemical-physical, functional, and product stability tests.

8. The sample size for the training set:

Not applicable. This device does not involve a training set as it's not an AI/ML model.

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

Not applicable.

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0.9% Sodium Chloride Flush Syringes are intended for use in flushing compatible intravenous administration sets and indwelling intravenous access devices. Use according to the recommendations of the manufacture for the appropriate device.

The Predicate Device, 510(k) Number: K984590 (Baxter Healthcare), consists of a sterile plastic syringe aseptically filled with 0.9% Sodium Chloride Flush Solution. The predicate device is fluid path sterile with a Sterility Assurance Level (SAL) of 10 . This is a single use device. AMUSA is the legal owner of the Baxter 510(k) K984590.

The Modified Device, the subject of this 510 (k), consists of a non-sterile plastic syringe filled with 0.9% Sodium Chloride Flush Solution that is sterilized by the addition of terminal sterilization (Radiation). The modified device is fluid path sterility Assurance Level (SAL) of 10 This is a single use device.

The provided text describes a 510(k) summary for a medical device called "0.9% Sodium Chloride Flush Syringe". This document is a premarket notification to the FDA to demonstrate that the modified device is substantially equivalent to a legally marketed predicate device.

Crucially, this document is for a medical device (syringe) and not an AI/ML software device. Therefore, the concepts of "acceptance criteria for an AI device", "study that proves the device meets the acceptance criteria", "sample size for test set", "ground truth", "number of experts", "adjudication method", "MRMC study", "standalone performance", and "training set" are not applicable in the context of this 510(k) submission.

The document focuses on demonstrating substantial equivalence through non-clinical verification testing due to a change in the sterilization process, not on the performance of a diagnostic or predictive algorithm.

However, based on the provided text, I can extract information related to the device and its assessment:

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

Since this is a non-AI/ML device, the concept of "acceptance criteria" is related to its physical, chemical, and functional properties, as well as its sterility and stability, rather than diagnostic performance metrics.

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

• Sample Size for Test Set: Not explicitly stated. The document refers to "non-clinical verification testing" which would involve a set of syringes, but the specific number tested is not provided in this summary.
• Data Provenance: Not applicable in the context of data for an AI/ML model. The testing was performed on the modified syringe product itself. Location of testing and specific methodology are not detailed in this summary.

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 AI/ML sense is not relevant here. The assessment is based on physical, chemical, and biological testing, not expert interpretation of outputs.

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

Not applicable. This is not a study requiring expert adjudication of outputs.

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 a medical device (syringe), not an AI/ML diagnostic tool involving human readers.

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

Not applicable. This is not an algorithm.

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

Not applicable. The "truth" for this device relates to meeting established engineering and sterility standards through laboratory testing (e.g., measuring chemical composition, mechanical strength, sterility testing results, stability over time).

8. The sample size for the training set

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

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

Not applicable.

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