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The Talon Transseptal Sheath is used for the percutaneous introduction of various types of cardiovascular catheters and guidewires to all heart chambers, including the left atrium via transseptal perforation/puncture.

The Talon Transseptal Sheath (“Sheath”) provides a conduit for catheterization and angiography of specific heart chambers and locations. It consists of a single lumen catheter with a proximal hemostasis valve and stopcock with flush port. A radiopaque marker is positioned near the distal tip to aid with fluoroscopic visualization. The Sheath is packaged with a dilator and two 60 cc syringes. The dilator is compatible with a 0.035" guidewire and has a tapered tip which aids insertion and positioning of the Sheath. The dilator attaches to the Sheath hemostasis valve, and a Y-connector with hemostasis valve connects to the dilator luer connector. A quick-release syringe is provided to connect to the Sheath sideport, and a standard VacLok syringe is provided to connect to the Sheath flush port and dilator connector.

The provided text describes the regulatory clearance for the Talon Transseptal Sheath. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study with acceptance criteria and device performance in the context of an AI/human reader study.

Therefore, many of the requested categories (e.g., sample size for test set, number of experts for ground truth, adjudication method, MRMC study, effect size, standalone performance, training set details) are not applicable or not provided in this document, as they relate to the evaluation of AI-driven diagnostic devices, which the Talon Transseptal Sheath is not.

Here's the information that can be extracted from the provided text, primarily focusing on the non-clinical testing section, which serves as the "study" for this device's regulatory submission:

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

The document states that "Test results demonstrated that all acceptance criteria were met," but it does not provide specific numerical acceptance criteria or reported device performance metrics for each test. It simply lists the types of tests performed.

2. Sample sized used for the test set and the data provenance

• Sample Size: Not specified for any of the non-clinical tests.
• Data Provenance: Not applicable in the context of device performance testing. These are laboratory/bench tests, not studies involving human 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 not an AI diagnostic device that relies on expert interpretation to establish ground truth for a test set. Its performance is evaluated through engineering and biological testing.

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

• Not Applicable. See explanation 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

• No. An MRMC study was not done as this is a medical device (transseptal sheath), not an AI-driven diagnostic or imaging aid.

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

• Not Applicable. This is not an algorithm or AI device. Its "standalone" performance is measured by its physical and biological properties.

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

• For Biocompatibility: Standards (ISO 10993-1) and validated laboratory test methodologies.
• For Sterilization: Standards (ISO 11135:2014/Amd 1:2018, AAMI TIR 28:2016) and validated sterilization processes.
• For Performance Tests: Established product specifications, engineering standards, and functional requirements, identified through a Design Failure Modes and Effects Analysis. The "ground truth" is adherence to these predefined criteria.

8. The sample size for the training set

• Not Applicable. This device does not use a training set as it is not an AI/machine learning product.

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

• Not Applicable. See explanation above.

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