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Indications for the Composite Hitch™ Suture Anchor include use in soft tissue reattachment procedures in the shoulder, wrist/hand, elbow, and knee. Specific indications are as follows:

Shoulder: Bankart repair, SLAP lesion repair, acromioclavicular separation repair, rotator cuff repair, capsule repair or capsulolabral reconstruction, biceps tenodesis, deltoid repair.

Wrist/Hand: Scapholunate ligament reconstruction

Elbow: Tennis elbow repair, Biceps tendon reconstruction, medial and lateral repairs, ulnar or radial collateral ligament reconstruction.

Knee: Extracapsular repair: Medial collateral ligament repair, lateral ligament repair, posterior oblique ligament repair, joint capsule closure, iliotibial band tenodesis, patellar ligament/tendon repair, vastus medialis obliquus (VMO) muscle advancement.

The Composite Hitch™ Suture Anchor is made with a composite, resorbable material. The soft tissue anchor, preloaded with polyethylene surgical suture, enables the implantable anchor by screwing it in through a tapped pre-drilled hole or by simply pushing the point directly into the bone without pre-drilling a starter hole.

This document is a 510(k) summary for the Biomet Composite Hitch™ Suture Anchor. It details the device, its intended use, and claims substantial equivalence to predicate devices. However, it explicitly states "Clinical Testing: None provided as a basis for substantial equivalence."

Therefore, the provided text does not contain information about acceptance criteria, device performance from a study, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth types, or training set details. The submission relies solely on non-clinical testing and comparison to legally marketed predicate devices.

Based on the provided text, the following information can be extracted:

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

   • N/A. The document states, "Clinical Testing: None provided as a basis for substantial equivalence." The basis for substantial equivalence relies on non-clinical testing and comparison to predicate devices, not on meeting specific acceptance criteria from a clinical performance study. The non-clinical testing "results indicate that the anchors are substantially equivalent to predicate anchors with similar indications for use," but no specific performance metrics or acceptance criteria are listed.

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

   • N/A. No clinical test set was used. Non-clinical laboratory testing was performed, but sample sizes for this testing are not provided in this summary.

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):

   • N/A. No clinical test set with human-established ground truth was performed.

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

   • N/A. No clinical test set was used requiring adjudication.

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:

   • N/A. No MRMC comparative effectiveness study was done, as no clinical testing was performed. This device is a suture anchor, not an AI-assisted diagnostic tool.

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

   • N/A. This device is a medical implant (suture anchor), not an algorithm.

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

   • N/A. No clinical study involving ground truth determination was performed. The non-clinical testing would likely have used engineering specifications or material properties as "ground truth" for evaluating the anchor's physical characteristics, but these are not detailed.

8. The sample size for the training set:

   • N/A. This device is a medical implant, not an AI/machine learning model. Therefore, there is no "training set."

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

   • N/A. Not applicable, as there is no training set for this device.

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