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The MedicineLodge ZipKnot™ is indicated for use in approximation and/or ligation of soft tissues using USP #2 braided polyethylene surgical suture.

The MedicineLodge ZipKnot™ consists of a Polyetheretherkeytone (PEEK) button threaded with high strength polyethylene suture Threaders and housed in a polypropylene Threader Pod. The suture is threaded through a series of holes in the ZipKnot™. The holes are designed in such as way as to allow suture to be pulled through the button in one direction but lock the suture if pulled in the opposite direction. Once suture is threaded through the button it can be advanced down the suture arms using the same techniques used in both open and endoscopic surgery when tying sliding surgical knots. Once the ZipKnot™ is in place the excess suture ends are trimmed.

The MedicineLodge ZipKnot™ will be provided sterile for single-use (ASTM 4169), packaged individually in a double tyek pouch. The device will be sterilized by Gamma irradiation (EN 552) or Ethylene Oxide (EN550) including limits for Ethylene Oxide residuals and validated to a sterility assurance level (SAL) of 106. The device is biocompatible per ISO-10993 and G95-1.

This device is a physical medical device (implantable clip/suture fixation device); therefore, the typical acceptance criteria and study designs for artificial intelligence/machine learning (AI/ML) software are not applicable. The provided text outlines the regulatory journey and key characteristics of the MedicineLodge ZipKnot™, focusing on its substantial equivalence to a predicate device rather than performance metrics against a "ground truth" as would be relevant for an AI/ML diagnostic tool.

However, based on the provided text, I can extract information relevant to how its safety and effectiveness were established for regulatory purposes, which can be interpreted in a broader sense as meeting "acceptance criteria":

Acceptance Criteria and Reported Device Performance (as demonstrated for regulatory clearance):

Based on the provided text, the following AI/ML-specific questions are not applicable or cannot be answered:

1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable for a physical medical device. Testing would involve mechanical, material, and biological evaluations, not a "test set" of data.
2. 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): Not applicable. "Ground truth" for this device would be established through bench testing, biocompatibility studies, and comparison to predicate device performance, not expert consensus on data.
3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
4. 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 as this is not an AI/ML diagnostic device.
5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For a physical device like this, "ground truth" would be established through:
   • Mechanical Testing: Demonstrating the device's ability to hold suture, withstand forces, and maintain integrity.
   • Biocompatibility Testing: According to ISO-10993, to ensure it doesn't cause adverse biological reactions.
   • Sterility Validation: Proving the sterilization method achieves the required Sterility Assurance Level (SAL).
   • Comparison to Predicate Device: Bench testing and performance data comparing it to the already approved predicate device (Smith & Nephew ULTRABRAID™ / I Feature Drive). The submission states, "The subject device was shown to have substantially equivalent performance when compared to the predicate device."
7. The sample size for the training set: Not applicable for a physical medical device.
8. How the ground truth for the training set was established: Not applicable.

In summary, the provided document is a 510(k) clearance letter for a physical medical device. The "acceptance criteria" revolve around demonstrating substantial equivalence to an existing predicate device through various non-clinical tests (mechanical, sterilization, biocompatibility) rather than through AI/ML performance metrics against a defined dataset and expert "ground truth."

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