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The Extension Set is intended for the conduction of fluids or medications.

The extension set is used to extend the length of another fluid or medication delivery device to provide continued fluid flow.

The Extension Set is a device used to supplement Epimed's current line of regional anesthesia catheters. The Extension Set consists of flexible plastic tubing, a male luer lock fitting on one end and a female luer lock fitting on the other end. Extension sets will be provided as a sterile, single use, disposable devices. The Extension Set will be available in a variety of lengths ranging from 12" to 60".

The provided document describes a 510(k) premarket notification for an "Extension Set" device and does not contain the information requested in the prompt regarding acceptance criteria, device performance tables, sample sizes for test/training sets, expert ground truth establishment, or specific study types like MRMC or standalone algorithm performance.

The document focuses on demonstrating substantial equivalence to predicate devices through bench testing of physical and technical characteristics.

Here's an analysis of what is provided and what is missing based on your prompt:

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

• Response: Not provided. The document mentions "bench testing performed on the Extension Set to compare performance characteristics to the predicate device(s) confirmed that the performance of the Epimed Extension Set is similar to that of the predicate device(s)." It lists the tested characteristics as "Fitting Bond Strength, Tubing Tensile Strength, Quantitative Flow, Qualitative Flow and Priming Volume." However, specific acceptance criteria values or detailed performance results (e.g., actual bond strength values, flow rates, etc.) are not included in the summary.

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

• Response: Not provided. The document states "bench testing" was performed but does not mention the sample size, type of study (retrospective/prospective), or data provenance.

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):

• Response: Not applicable. This type of information is relevant for AI/software devices where human expert consensus is used to establish ground truth for classification tasks. The device described is a physical medical device (extension set), and its performance is evaluated through bench testing against physical and technical characteristics, not through expert-reviewed data.

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

• Response: Not applicable. See point 3.

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:

• Response: Not applicable. The device is a physical extension set, not an AI or imaging diagnostic tool that would involve human readers or AI assistance.

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

• Response: Not applicable. See point 5.

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

• Response: For this physical device, the "ground truth" or reference standard for evaluation would be the specified physical and technical requirements or performance characteristics of the predicate devices. The document implies that the Epimed device met these unspecified performance characteristics of the predicate devices.

8. The sample size for the training set:

• Response: Not applicable. There is no training set mentioned, as this is a physical device submission, not an AI/machine learning device.

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

• Response: Not applicable. See point 8.

In summary, the provided document is a 510(k) summary for a physical medical device (an extension set) that demonstrates substantial equivalence through bench testing to predicate devices. It does not contain the detailed study design, acceptance criteria, sample sizes, or ground truth establishment methods typically found in submissions for AI/software devices.

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