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For nasogastric/oralgastric enteral feeding, incorporating safety connectors which reduce the risk of misconnections between feeding tubes and intravenous connectors.

The Nutrisafe 2 feeding tubes are a product line extension to the existing Nutrisafe 2 feeding tubes. The subject feeding tubes are available in several sizes and in two materials. The feeding system contains a unique connection that does not incorporate a luer, reducing the risk of inadvertently connecting to intravenous connectors. The locking connection reduces the risk of involuntary disconnection; voluntary disconnection is achieved by simply unscrewing the hub connections.

The provided text describes a 510(k) premarket notification for a medical device, the Nutrisafe 2 Feeding Tube, and its substantial equivalence to a predicate device. However, it does not involve an AI/ML device or a study that evaluates its performance against acceptance criteria in the way described in your request. The document focuses on the regulatory clearance process for a common medical device (feeding tube) based on its technological characteristics and intended use, rather than an AI algorithm's diagnostic or predictive capabilities.

Therefore, many of the requested categories in your prompt are not applicable to the provided information. I will, however, outline what can be extracted from the text in relation to your request.

Summary of Provided Information Regarding Acceptance Criteria and Study:

The provided document, K100163, is a 510(k) summary for a medical device (Nutrisafe 2 Feeding Tube), which is a product line extension. This type of regulatory submission assesses substantial equivalence to a predicate device, rather than proving performance against specific quantitative acceptance criteria of an AI model.

The "study" mentioned is a non-clinical verification through in-vitro bench testing. This type of testing ensures the device meets its specifications and intended use, primarily focusing on physical and functional characteristics suitable for a feeding tube, such as the integrity of the connection system, material compatibility, and flow rates. It does not involve human subjects, comparison to AI, or expert adjudication of outcomes.

Here's how the information aligns (or doesn't align) with your request categories:

1. Table of Acceptance Criteria and Reported Device Performance: This kind of quantitative data (e.g., sensitivity, specificity, AUC) is not present because this is a physical medical device, not an AI/ML diagnostic tool. The general "acceptance criteria" for this device would be its ability to meet specifications for a feeding tube as determined by the in-vitro bench testing.

   Metric/Criteria	Reported Device Performance
   All Specifications and Intended Use	"meets all specifications and intended use."

2. Sample size used for the test set and the data provenance: Not applicable in the context of an AI/ML device. The "test set" here would refer to the physical samples of the Nutrisafe 2 Feeding Tubes used in the in-vitro bench testing. The document does not specify the number of tubes tested, nor their manufacturing origin beyond "Vygon, Montgomeryville, PA 18936." This was in-vitro testing, not human data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth, in the context of an AI/ML model, refers to validated diagnoses or outcomes. For a physical feeding tube, "ground truth" is established by engineering standards and functional tests. No external experts or adjudication were involved in establishing "ground truth" for the device's functional performance beyond the internal verification process.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable. This method is used for resolving discrepancies in expert labeling or diagnoses for AI/ML validation datasets.

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 study type is for evaluating the impact of AI assistance on human diagnostic performance. The Nutrisafe 2 Feeding Tube is a standalone medical device, not an AI component.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. This refers to the performance of an AI algorithm alone.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For this device, the "ground truth" for its performance is derived from engineering specifications and in-vitro functional and safety testing results. For example, the "locking connection reduces the risk of involuntary disconnection" is a functional characteristic tested against design requirements.

8. The sample size for the training set: Not applicable. This device does not have a "training set" in the context of AI/ML.

9. How the ground truth for the training set was established: Not applicable.

In summary, the provided document describes a traditional medical device (feeding tube) and its regulatory clearance based on substantial equivalence and non-clinical bench testing, not an AI/ML device. Therefore, most of the detailed questions regarding AI/ML study methodologies are not relevant to this specific premarket notification.

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