Search Results

The Pajunk Plexolong sets are intended for delivery of continuous conduction anesthesia of peripheral nerves and plexus for up to 72 hours. Continuous delivery for up to 72 hours is accomplished using the Polyamide indwelling catheter. An electrical stimulus may be applied to the conduction needle via a cable and connectors to assist the physician pinpoint the area of application.

The PAJUNK Plexolong sets contain single use sterile and non-pyrogenic needles with tubing. with or without a plastic cannula and catheters intended for delivery of continuous conduction anesthesia of peripheral nerves and plexus for up to 72 hours. Continuous delivery is accomplished using the catheter. An electrical stimulus may be applied to the conduction needle via a cable and connector to assist the physician pinpoint the area of application.

This 510(k) summary describes a medical device, specifically an Anesthesia Conduction Kit. For this type of device, "acceptance criteria" are typically defined by adherence to recognized consensus standards and "device performance" is demonstrated through testing to ensure conformance to these standards, as opposed to clinical performance metrics for an AI-enabled device.

Let's break down the information requested based on the provided document:

Description of Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

The PAJUNK Plexolong Sets are designed to conform to recognized national and international consensus standards relevant to medical devices, particularly those for needles, cannulas, Luer fittings, sterilization, packaging, and risk analysis. The study demonstrating that the device meets these acceptance criteria involved performance testing to verify conformance to these applicable standards.

1. Table of Acceptance Criteria and Reported Device Performance

Regarding AI/Machine Learning Specific Questions:

The provided document describes a conventional medical device (anesthesia conduction kit) and does not refer to any AI or machine learning components. Therefore, the following AI/ML-specific questions are not applicable to this submission.

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

• Not applicable. This document describes a physical medical device, not an AI/ML algorithm or software that uses test data in the conventional sense. Performance testing was conducted on samples of the physical device according to industry standards.

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)

• Not applicable. Ground truth in the context of expert consensus is not relevant for this physical device. Performance is assessed against engineering and material specifications as defined by consensus standards.

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

• Not applicable. This relates to AI/ML model performance evaluation, not a physical medical device.

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 device does not involve human "readers" or AI assistance.

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

• Not applicable. There is no algorithm or software component described.

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

• For this device, "ground truth" is established by the specifications outlined in the referenced consensus standards. Device components (e.g., Luer fittings, cannula dimensions, material properties, sterility assurance) are tested against these published, accepted industry standards.

8. The sample size for the training set

• Not applicable. This device does not involve a training set for an AI/ML model.

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

• Not applicable. This device does not involve a training set for an AI/ML model.

Ask a specific question about this device