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The Biomet JuggerLoc™ Bone system is intended for repair in the foot and ankle including indications for: Midfoot repair including but not limited to Lisfranc repair, ankle syndesmosis fixation (syndesmosis disruptions), and as an adjunct in connection with trauma hardware for Weber B and C ankle fractures.

The new JuggerLoc Bone to Bone system is a suture based construct intended to stabilize fractures. The construct consists of a JuggerKnot Soft Anchor with ZipLoop Technology, featuring combinations of suture and metal button fixation.

The provided text describes a medical device called the "JuggerLoc Bone to Bone System," a suture-based construct intended to stabilize fractures. The submission is a 510(k) for a new device construct, claiming substantial equivalence to predicate devices (K130033 - Biomet ToggleLoc System, K110145 - Biomet JuggerKnot Soft Anchors, K133275 - Arthrex Mini-Tightrope).

Here's an analysis of the provided information regarding acceptance criteria and the study that proves 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 quantitative or numerical format (e.g., a specific tensile strength in Newtons, or a particular displacement under load). Instead, it refers to "Preclinical performance tests are provided to address the subject construct's strength." The general acceptance is that "Results indicate that the subject construct is substantially equivalent to legally marketed devices."

Therefore, a table cannot be fully constructed with explicit numerical criteria. However, based on the text, the performance goal is implicitly to meet or exceed the performance of the predicate device(s) in terms of strength.

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 mentions "Preclinical performance tests" but does not specify the sample size used for these tests. It also does not specify the data provenance beyond stating they are "Preclinical performance tests," implying laboratory testing rather than human subject data. There is no information on country of origin or whether it was retrospective or prospective.

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 section is not applicable as the study described is a preclinical performance test (likely mechanical and material testing), not a study requiring human expert assessment of a test set, like in imaging or diagnostic device evaluations. There is no "ground truth" to be established by experts in the context of this submission.

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

This is not applicable for the same reasons as point 3. No human expert adjudication was involved in these preclinical performance tests.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This type of study is relevant for diagnostic devices involving human interpretation, not for mechanical fixation systems like the JuggerLoc Bone to Bone System. There is no mention of AI assistance.

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 medical implant, not an algorithm or software. Therefore, an algorithm-only standalone performance evaluation is not relevant.

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

For the preclinical performance tests, the "ground truth" would be objective physical measurements of the device's mechanical properties (e.g., tensile strength, displacement, fatigue resistance), compared against established industry standards or the performance of predicate devices. The document does not specify the exact parameters measured, but implies mechanical strength testing as the basis for evaluation.

8. The sample size for the training set

This is not applicable. There is no mention of a "training set" as this device is a physical construct, not a learning algorithm or AI.

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

This is not applicable for the same reason as point 8.

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