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The NeoMed Polyurethane Feeding Tube is intended for nasogastric or orogastric delivery of various types of liquid nutritional media through the gastro intestinal tract of neonatal and small pediatric patients, and is not intended for use beyond 30 days.

The NeoMed Polyurethane Feeding Tube is a polyurethane single lumen catheter that is used to deliver liquid nutritional media. The device consists of the following main components: a feeding tube single lumen catheter, a hub with tethered connection closure plug, and a connector.

The provided text describes the submission for a 510(k) premarket notification for the "NeoMed Polyurethane Feeding Tube". This document focuses on demonstrating substantial equivalence to a predicate device, not on proving device performance against specific acceptance criteria through a clinical study or detailed standalone algorithm performance.

Here's a breakdown of why many of your requested points cannot be answered from this document:

• This is not an AI/ML device. The device is a physical medical device (a feeding tube). Therefore, concepts like AI assistance, human readers, effect size, training sets, and sophisticated ground truth establishment are not applicable.
• The approval is based on substantial equivalence, not a new clinical study with acceptance criteria. The document explicitly states that "Bench testing has demonstrated that the NeoMed Polyurethane Feeding Tube is functionally equivalent to predicate NeoMed Silicone Enteral Feeding Tube, and that any minor differences do not affect safety or effectiveness."

Therefore, I cannot provide a detailed answer to many of your questions in the format you requested, as the information is not present in the provided text, and the nature of the device and approval pathway does not align with your questions.

However, I can extract the relevant information regarding the "performance data" mentioned:

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

The document does not specify formal, quantitative "acceptance criteria" with numerical targets in the context of a clinical study or software performance metrics. Instead, "performance data" refers to design verification testing to demonstrate equivalence.

2. Sample size used for the test set and the data provenance
This information is not provided. The document mentions "bench testing" and "design verification performance test results," but does not specify sample sizes or data provenance for these tests. It is likely referring to laboratory-based engineering tests rather than patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. This device is a physical medical tube, not an AI/ML-based diagnostic or imaging device requiring expert ground truth for a test set.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable, for the same reasons as above.

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 not an AI-assisted device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable; this is a physical medical device, not an algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
For a device relying on bench testing for substantial equivalence, the "ground truth" would be engineering specifications, material standards (like ISO 10993 for biocompatibility), and functional performance metrics compared against the predicate device, rather than clinical outcomes or expert consensus on disease states.

8. The sample size for the training set
Not applicable; this is not an AI/ML device that uses a training set.

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

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