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The Sleeve with ZipLoop™ Fixation Devices are intended for fixation for the following indications:

Shoulder
Bankart lesion repair, SLAP lesion repair, Acromio-clavicular repair, Capsular shift/capsulolabral reconstruction, Deltoid repair, Rotator cuff tear repair, Biceps tenodesis

Foot and Ankle
Medial/lateral repair and reconstruction, Mid- and forefoot repair, Hallux valgus reconstruction, Metatarsal ligament/tendon repair or reconstruction, Achilles tendon repair

Elbow
Ulnar or radial collateral ligament reconstruction, Lateral epicondylitis repair, Biceps tendon reattachment

Knee
Extra-capsular repair: MCL, LCL, and posterior oblique ligament Iliotibial band tenodesis, Patellar tendon repair, VMO advancement, Joint capsule closure

Hand and Wrist
Collateral ligament repair, Scapholunate reconstruction, Tendon transfers in phalanx, Volar plate reconstruction

Hip
Acetabular labral repair

The Sleeve with ZipLoop™ Fixation Devices are a three component assembly consisting of two sleeves and a ZipLoop™ construct. The ZipLoop™ construct is an adjustable loop created with a single piece of fiber material. When the ZipLoop is pulled tight, the sleeve locks against the bone fixating the soft tissue. The Sleeve with ZipLoop™ Fixation Devices provide the surgeon with a means to aid in the management of soft tissue to bone reattachment procedures.

The provided text describes a 510(k) summary for the "Sleeve with ZipLoop™ Fixation Devices". This document focuses on demonstrating substantial equivalence to predicate devices rather than proving specific performance against acceptance criteria through a clinical study. Therefore, much of the requested information (like acceptance criteria, sample sizes for test/training sets, expert details, adjudication methods, MRMC studies, standalone performance, and ground truth establishment) is not applicable or not provided in this type of submission.

Here's a breakdown of what can be extracted and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation: The document explicitly states that non-clinical laboratory testing was performed to verify the fixation strength and functionality. The acceptance criterion was implicitly equivalence in pullout strength to the predicate devices.

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

• Sample Size: Not specified. The document only mentions "non-clinical laboratory testing" without detailing the number of samples or tests performed.
• Data Provenance: Not applicable, as this was non-clinical laboratory testing, not human data. The testing was performed by Biomet Sports Medicine.

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 clinical studies and expert review for AI devices, is not relevant here. The evaluation was based on physical property testing (pullout strength).

4. Adjudication method for the test set

• Not Applicable. Adjudication methods are used in clinical studies or expert reviews to resolve disagreements, not for mechanical testing.

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

• No. An MRMC study was not done. This device is a surgical fixation device, not an AI-powered diagnostic or decision support tool. The document explicitly states: "Clinical Testing: None provided as a basis for substantial equivalence."

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

• Not Applicable. This is a physical medical device, not an algorithm. Standalone performance refers to AI algorithms.

7. The type of ground truth used

• Mechanical Test Results (Pullout Strength): The "ground truth" for the non-clinical testing was the objective measurement of pullout strength, compared against the pullout strength of predicate devices.

8. The sample size for the training set

• Not Applicable. This device did not involve machine learning or AI, so there was no training set. The "training" for such devices would typically involve design and engineering processes, material selection, and iterative physical testing, which don't have "training sets" in the AI sense.

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

• Not Applicable. No training set as it's not an AI/ML device.

In summary, this 510(k) submission primarily relies on non-clinical laboratory testing to demonstrate mechanical equivalence to existing predicate devices, rather than clinical efficacy or AI performance metrics.

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