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The ProSeal™ Closed System drug Transfer Device (CSTD) mechanically prohibits environmental contaminants from entering the system and the escape of drug or vapor concentrations from thereby minimizing individual and environmental exposure to drug vapor, aerosols, and spills. The ProSeal™ system also prevents the introduction of microbial contaminations into the drug or fluid path for up to 7 days when used as intended.

This FDA Submission is to include an authorization to legally market the ProSeal" Vented Universal Vial Adaptor to the existing ProSeal™ CSTD devices system of eleven (11) devices that were cleared for sales in the US, the most recent being the ToxiSeal™ Vial Adaptor (K241823).

The ProSeal™ Vented Universal Vial Adaptor is a component of the ProSeal™ CSTD system intended for connection with the interface membranes between any standard vials and ProSeal™ CSTD component devices for close system fluid transfer. When connected to a standard vial and engaged with a ProSeal™ Injector (Syringe Adaptor), fluid can be transferred to and from the connecting component device in a closed system. The ProSeal™ Vented Universal Vial Adaptor and its corresponding interface membranes exhibit a dry connection with the communicating surfaces in a fluid transfer. The use of this component and its appropriate ProSeal™ CSTD component device reduces the risk of microbial ingress for up to 168 hours (or 7 days). when used as intended.

The closed transfer of liquid that takes place with the use of the ProSeal™ CSTD system as follows:

• A double membrane septum design utilizing self-sealing elastomeric membranes tightly fits . together when the system components engage. A cannula within the ProSeal™ Injector housing perforates the double membrane for the transfer of liquid. When the cannula is retracted, the membranes seal off the transfer of environmental contaminants into the system and/or escape of drug or vapor concentrations outside the system, thereby minimizing the individual and environmental exposure to drug vapor, aerosols, and also minimizing the risk of microbial contamination.

The provided text is a 510(k) summary for the ProSeal™ Vented Universal Vial Adaptor. It outlines the device description, indications for use, comparison to a predicate device, and performance data supporting substantial equivalence. However, it does not describe acceptance criteria for an AI/ML-enabled medical device performance study, nor does it provide details on the specific study that would prove such a device meets acceptance criteria.

The document pertains to a physical medical device (a vial adaptor) and its mechanical, material, and sterility performance, not a software-based AI/ML device for which metrics like sensitivity, specificity, or AUC would be relevant. Therefore, most of the requested information (sample size for test set, data provenance, expert ground truth, MRMC study, standalone performance, training set details) is not applicable to this 510(k) submission.

The closest analogous information in this document relates to functional performance testing and biocompatibility testing for the physical device.

Here's how to interpret the available information in the context of the prompt, acknowledging the mismatch:

The document effectively describes the "acceptance criteria" and "study" for a physical medical device based on regulatory standards and comparison to a predicate, not an AI/ML device.

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

The document doesn't present a table of quantitative acceptance criteria in the typical AI/ML sense (e.g., target AUC, sensitivity). Instead, it states that the device was tested and demonstrated to be in conformance with recognized ISO and FDA standards, and that "Performance testing demonstrates that this difference [in air-vent size] does not raise new questions regarding safety and effectiveness." The acceptance criteria for this physical device are the successful completion and conformance to these standards and the demonstration of substantial equivalence to the predicate.

Here's a reinterpretation of their "acceptance criteria" based on the provided text, focusing on the functional performance tests listed:

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

• Sample size: Not explicitly stated as a numerical sample size for each functional test. The document mentions "testing data on devices cleared under K241823" and "testing data on device cleared under K222929," implying samples were used according to the referenced standards. For physical device testing, sample sizes are usually quite small compared to AI/ML datasets (e.g., n=3-10 per test for validation).
• Data Provenance: Not explicitly stated (e.g., country of origin). The testing would have been done retrospectively based on the device design.

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

• Not applicable. This device's performance is assessed against physical and biological characteristics, not subjective interpretations requiring human expert consensus. The "ground truth" is defined by the objective measurements and parameters outlined in the referenced international standards (e.g., ISO, ASTM, USP).

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

• Not applicable. This is not a human-in-the-loop or interpretation-based study.

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 physical device, not an AI-enabled diagnostic tool for human readers.

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

• Not applicable. This is a physical device.

7. The type of ground truth used

• The "ground truth" for this device's performance is established by objective measurements and chemical/physical/biological testing according to highly detailed and recognized pre-defined international standards (ISO, ASTM, USP, ANSI/AAMI, NIOSH protocols). It's not based on expert consensus, pathology, or outcomes data in the way an AI diagnostic would be. For instance, the "ground truth" for microbial ingress is whether microbes actually ingress, measured per specified test protocols.

8. The sample size for the training set

• Not applicable. This is a physical device, not an AI/ML algorithm requiring a "training set." The design and manufacturing processes are validated, but there's no data training in the AI sense.

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

• Not applicable, as there is no "training set."

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