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To be connected to suction / vacuum assemblies to filter smoke particulates / aerosols, and retain aqueous fluids generated during laparoscopic electrosurgery or laser surgery.

Hydrophobic multilayered filter with vacuum tubing for attachment to wall vacuum or suction source.

This document is a 510(k) summary for the Davol Plume Filter and focuses on demonstrating substantial equivalence to a predicate device, the Contain™ Hydrophobic Safety Filter, rather than establishing specific acceptance criteria for a new device's performance through a dedicated study.

Therefore, the provided text does not contain the information needed to answer many of your questions, as it describes a different type of regulatory submission. There is no independent study described here that measures device performance against acceptance criteria in the way you've outlined.

However, I can extract the relevant information where available:

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

The document primarily focuses on establishing "substantial equivalence" to a predicate device, not on setting new performance acceptance criteria for a novel device. The key performance claim made is:

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

This information is not provided in the document. The submission relies on the established performance of the predicate device.

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)

This information is not provided in the document. This type of expert assessment is not relevant for a 510(k) submission focused on substantial equivalence based on design and materials.

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

This information is not provided in the document.

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

This information is not applicable and not provided. This document describes a physical filter, not an AI-powered diagnostic device, so MRMC studies are not relevant.

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

This information is not applicable and not provided. This document describes a physical filter, not an algorithm.

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

The document does not describe a "ground truth" establishment in the context of a new performance study. It relies on the established performance of the predicate device (Contain™ Hydrophobic Safety Filter), which presumably had its performance validated through testing by a similar claim.

8. The sample size for the training set

This information is not applicable and not provided. There is no "training set" as this is not an AI/machine learning device.

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

This information is not applicable and not provided.

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