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The Mini Stick ENVI Non-Vascular Introducer Kit is utilized to facilitate the introduction and placement of a 0.035 in (0.89 mm) or 0.038 in (0.97 mm) diameter guidewire for non-vascular procedures.

The Mini Stick ENVI Non-Vascular Introducer Kit contains one or more of the following components. Reference unit label for specific devices: Triaxial Sheath/Dilator assembly with stiffening cannula; 18G (1.4 mm) or 21G (0.9 mm) needle with stylet; 0.018 in (0.46 mm) floppy tip guidewire; 0.035 in (0.89 mm) or 0.038 in (0.97 mm) heavy duty guidewire. For user convenience, all kit components are also available in multiple kit configurations as sterile, standalone devices.

The provided document is a 510(k) Premarket Notification for a medical device called the "Mini Stick ENVI Non-Vascular Introducer Kit." This document focuses on demonstrating substantial equivalence to a predicate device based on technological characteristics and non-clinical performance data.

Therefore, the concepts of acceptance criteria for diagnostic performance, a test set, expert ground truth, adjudication methods, MRMC studies, standalone performance, and training set information are not applicable to this type of submission. This device is an introducer kit, and the evaluation is primarily focused on its physical and functional characteristics, not on diagnostic accuracy of an AI or image-based system.

Here's an analysis based on the information provided, explaining why certain sections of your request are not relevant:

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

The document does not explicitly state "acceptance criteria" in the context of diagnostic performance metrics like sensitivity, specificity, accuracy, etc., because it's a physical medical device, not a diagnostic algorithm. However, it lists performance tests conducted to demonstrate the device's functional integrity and safety. We can infer the "acceptance criteria" were successful completion of these tests in accordance with the cited ISO standards.

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

Not applicable. This device is not a diagnostic tool or algorithm that uses a "test set" of patient data. The "tests" mentioned are non-clinical engineering and material tests, which would involve specific numbers of device components or assemblies, not patient samples or data provenance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. Ground truth, in the context of diagnostic performance, is not relevant here. The "truth" in this context is whether the device physically meets its intended specifications and safety standards, determined by engineering and scientific testing, not expert interpretation of diagnostic images or clinical data.

4. Adjudication method for the test set

Not applicable for the same reasons as above. Adjudication methods are used to establish a consensus ground truth from multiple expert interpretations, which is not relevant for the functional testing of a physical introducer kit.

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. MRMC studies are for evaluating the performance of diagnostic systems (often AI-assisted) with human readers. This device is an introducer kit, not a diagnostic system.

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

Not applicable. There is no algorithm associated with this device.

7. The type of ground truth used

Not applicable in the diagnostic sense. The "ground truth" for this device's performance is established by its manufacturing specifications and compliance with international standards for medical devices (e.g., ISO 11070, ISO 594-2, ISO 10993-1) as demonstrated through physical and chemical testing.

8. The sample size for the training set

Not applicable. This is not an AI/ML device; there is no "training set."

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

Not applicable. As there is no training set for an AI/ML model, there is no ground truth to be established for it.

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