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Monosyn Quick Synthetic Absorbable Surgical Suture is intended for general soft tissue approximation of the skin and mucosa, where only short term wound support (6-7 days) is required. Monosyn Quick suture is not indicated for use in cardiovascular or neurosurgery.

Monosyn Quick is an absorbable flexible monofilament suture which is supplied sterile. It is composed of a synthetic polyglycolic acid-based copolymer. The Monosyn Quick suture is undyed and will be offered in diameters ranging from USP size 6-0 through 1. It is uncoated and will be available in a variety of cut lengths with or with out needles attached.

The provided text describes the regulatory filing for a medical device, the Monosyn Quick Synthetic Absorbable Surgical Suture, and outlines the testing performed to demonstrate its substantial equivalence to predicate devices. However, the document does not detail a study proving the device meets "acceptance criteria" in the way one might expect for a diagnostic or AI-driven system. Instead, it focuses on demonstrating compliance with recognized standards and substantial equivalence to existing devices for safety and performance.

Therefore, many of the requested categories for acceptance criteria and study details are not directly applicable to this type of regulatory submission (510(k) for a surgical suture). I will answer the questions based on the closest available information in the document.

Here's a breakdown of the information that can be extracted:

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

The document doesn't explicitly state "acceptance criteria" as defined by specific thresholds for performance metrics. Instead, it refers to compliance with established USP standards for surgical sutures. The performance data is presented as satisfactory and demonstrating equivalence.

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

The document does not specify the sample sizes for the mechanical, biocompatibility, or resorption tests. It refers to "testing" and "data generation" but not specific numbers of samples. Data provenance is not mentioned beyond the tests being performed to support the 510(k) submission.

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

This concept is not applicable here. Surgical sutures are evaluated against engineering and biological standards, not through expert consensus on qualitative "ground truth" like in AI/diagnostic imaging.

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

Not applicable. This type of adjudication is used for establishing ground truth in human-AI studies, not for the evaluation of a physical medical device like a suture.

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 a surgical suture, 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 a surgical suture, not a standalone algorithm.

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

The "ground truth" equivalent in this context is the adherence to established industry standards and regulations:

• USP (United States Pharmacopeia) 39 Monograph for Absorbable Surgical Sutures: Provides specifications for material, diameter, tensile strength, and other physical properties.
• ISO (International Organization for Standardization) 10993 series and 11135-1: Provide standards for biological evaluation (biocompatibility) and sterilization processes for medical devices.
• Predicate Device Performance: Comparison to existing legally marketed devices serves as a benchmark for safety and effectiveness.

8. The sample size for the training set

Not applicable. Medical devices like sutures are not "trained" in the machine learning sense. The manufacturing process is established and controlled, but there isn't a "training set" of data in the way an AI model would have one.

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

Not applicable. As noted above, this device does not utilize machine learning or require a training set with established ground truth in that context.

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