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The Vial adapter with filter is intended for use by Healthcare Professionals for the transfer and mixing of drugs contained in vials within clinical, hospital, or healthcare environments.

The sterile device pierces the elastomeric septum of a drug vial with its integrated piercing spike. The device is then pushed fully onto the drug vial and seats securely around the ferrule of the drug vial utilizing the housing of the vial adapter. The connector on opposite side of the vented vial adapter is for the connection of a standard luer syringe for the reconstitution and removal of the contents of the drug vial.

The device is intended for use by Healthcare Professionals (HCPs) in a clinical, hospital, or other healthcare environment. The subject device is available by prescription use only and has no known contraindications.

The proposed vented vial adapter is available in 13mm, 20mm diameter to accommodate respective sizes of drug vials.

This document is a 510(k) Premarket Notification from the FDA regarding a Vial Adapter with Filter. It does not present information about an AI/ML medical device, but rather a physical medical device. Therefore, the request to describe acceptance criteria and a study proving an AI/ML device meets these criteria cannot be fully answered from the provided text.

The provided text details the regulatory approval process for a physical medical device (Vial Adapter with Filter) and its substantial equivalence to a predicate device. It focuses on the physical, chemical, and biological performance of the device, rather than the performance of an AI/ML algorithm.

However, I can extract the general types of performance criteria and testing mentioned for this physical device, and point out what information is missing to answer the AI/ML-specific questions.

Based on the provided text, here's what can be inferred about the physical device's "acceptance criteria" and "study" (non-AI/ML context), and why AI/ML specific questions cannot be answered:

Device: Vial Adapter with Filter (physical medical device)
Regulatory ID: K233284

1. A table of "Acceptance Criteria" and "Reported Device Performance":

For a physical device, acceptance criteria are typically defined by compliance with recognized standards and internal performance specifications. The "reported device performance" are the results of tests conducted against these standards/specifications.

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

• The document does not specify the sample sizes for any of the performance tests. It only states that "performance data were provided."
• Data Provenance: The manufacturer is Hangzhou Qiantang Longyue Biotechnology Co., LTD, based in China (implied by address and country code in phone number). The nature of the studies (retrospective/prospective) for a physical device's performance testing is implicitly "prospective" as new devices would be tested to these standards.

Regarding AI/ML specific questions (which are not applicable to this physical device):

The following points are not addressed in the provided document because it pertains to a physical medical device, not an AI/ML algorithm:

• Number of experts used to establish the ground truth for the test set and their qualifications: Not applicable. Ground truth for a physical device is established through direct measurements, chemical analyses, and biological assays against established standards, not expert annotation of data.
• Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.
• If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance: Not applicable. This is a study design for AI-assisted diagnostic devices.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For a physical device, ground truth is based on the results of validated physical, chemical, and biological tests according to international and internal standards (as listed in the table above).
• The sample size for the training set: Not applicable. This device is not an AI/ML algorithm.
• How the ground truth for the training set was established: Not applicable. This device is not an AI/ML algorithm.

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