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1. The pRESET LITE Thrombectomy Device is indicated for use to restore blood flow in the neurovasculature by removing thrombus for the treatment of acute ischemic stroke to reduce disability in patients with a persistent, proximal anterior circulation, large vessel occlusion, and smaller core infarcts who have first received thrombolytic therapy. Endovascular therapy with the device should be started within 6 hours of symptom onset.

2. The pRESET LITE Thrombectomy Device is indicated to restore blood flow by removing thrombus from a large intracranial vessel in patients experiencing ischemic stroke within 8 hours of symptom onset. Patients who are ineligible for thrombolytic therapy or who fail thrombolytic therapy are candidates for treatment.

The pRESET® LITE Thrombectomy Device is designed to restore blood flow in the neurovasculature by mechanical removal of thrombus in patients experiencing acute ischemic stroke due to large vessel occlusion with thrombus. The device is designed for use in large vessels of the neurovasculature such as the internal carotid artery (ICA) and the middle cerebral artery (MCA). The device is supplied sterile and intended for single use only.

The provided document is a 510(k) summary for the phenox pRESET LITE Thrombectomy Device. It details the device's indications for use, comparison to a predicate device, and performance data to support substantial equivalence.

Based on the nature of this document (a 510(k) summary for a thrombectomy device), it does not describe a study involving an AI/Machine Learning device that requires a test set, ground truth experts, or MRMC studies. The performance data presented are for bench testing and biocompatibility, which are typical for physical medical devices, not AI software.

Therefore, many of the requested criteria such as "Sample sized used for the test set", "Number of experts used to establish the ground truth", "Adjudication method", "MRMC comparative effectiveness study", "Standalone performance", "Type of ground truth used", "Sample size for the training set", and "How the ground truth for the training set was established" are not applicable to this document's content.

The document explicitly states:

• "No animal testing was required to support substantial equivalence."
• "No clinical testing was required to support substantial equivalence."

This confirms that the assessment relies on non-clinical (bench) performance data and a comparison to a predicate device, rather than data from human or animal studies with clinical endpoints or AI performance metrics.

However, I can extract the acceptance criteria and reported device performance from the "Non-Clinical Performance Data - Bench" section.

Acceptance Criteria and Reported Device Performance (as extrapolated from Bench Testing)

The document primarily relies on engineering and material science-based acceptance criteria for a physical medical device. The "Conclusions" column in Table 5 serves as the reported device performance, indicating a "Pass" for each test, implying the acceptance criteria were met.

1. Table of Acceptance Criteria and the Reported Device Performance:

2. Sample sized used for the test set and the data provenance: Not applicable. This document describes physical bench testing for a mechanical device, not a data-driven AI model.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for a physical medical device's bench testing is typically established by engineering specifications and objective measurements, not expert consensus readouts.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
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 for a mechanical thrombectomy device, not an AI-assisted diagnostic tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For the performance data presented, the "ground truth" (or acceptance criteria) is based on engineering specifications, standardized test methods (e.g., ASTM, ISO), and performance expectations for a physical medical device in simulated environments.
8. The sample size for the training set: Not applicable. This is a physical device, not an AI model.
9. How the ground truth for the training set was established: Not applicable.

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