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SYNTHECEL® Dura Repair is indicated as a dura replacement for the repair of dura mater in adults.

SYNTHECEL® Dura Repair is composed of biosynthesized cellulose and water with a unique construction of non-woven, interconnected cellulose fibers. SYNTHECEL® Dura Repair functions as a mechanical layer which protects and repairs the dural defect while preventing further CSF leakage. SYNTHECEL® Dura Repair is immunologically inert and has demonstrated minimal foreign body response. It is non-resorbable.

The document provided is a 510(k) premarket notification for the SYNTHECEL Dura Repair device, seeking substantial equivalence to previously cleared predicate devices. It describes the device, its intended use, and the non-clinical performance data used to support its equivalence.

Here's an analysis of the provided text in relation to your request about acceptance criteria and a study proving the device meets them:

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

The document does not explicitly state "acceptance criteria" in a table format with specific numerical targets. Instead, it relies on demonstrating substantial equivalence to predicate devices through mechanical testing and biocompatibility. The reported performance is a statement of equivalence rather than meeting pre-defined numerical thresholds for a novel device.

However, based on the "Non-Clinical Performance Data" section, we can infer the areas of evaluation and the general outcome:

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

The document does not provide specific sample sizes for the mechanical testing (e.g., number of samples for burst strength or suture pull-out strength). It also does not specify the country of origin of the data or whether the non-clinical performance data was retrospective or prospective. It simply states that "Mechanical testing data was collected," implying lab-based testing.

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 applicable to the provided document. The submission is for a medical device (dura repair), and the "test set" in this context refers to physical samples undergoing mechanical and biological evaluations, not diagnostic outputs requiring expert interpretation for ground truth establishment.

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

This information is not applicable. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies or studies involving human interpretation of data (e.g., medical imaging) to resolve discrepancies. The provided document focuses on non-clinical performance data of a physical 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:

This is not applicable. The device is SYNTHECEL Dura Repair, a physical medical device, not an AI or diagnostic tool that would involve human readers or MRMC studies.

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

This is not applicable. The device is a physical dura repair product, not an algorithm.

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

For the non-clinical performance data:

• Mechanical Testing (Burst Strength, Suture Pull-Out Strength): The "ground truth" is established through standardized laboratory testing methods that quantify physical properties according to accepted engineering and medical device standards. The comparison is against predicate device performance, implying the predicate's performance serves as the benchmark.
• Biocompatibility: The "ground truth" is established by adhering to international standards (ISO 10993-1) which define acceptable biological responses (e.g., non-irritating, non-cytotoxic).

8. The sample size for the training set:

This is not applicable. The submission is for a physical medical device, not an AI or machine learning model that requires a training set.

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

This is not applicable, as there is no training set for a physical medical device.

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