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The Tarsus Synde-Lock Syndesmosis Repair Kit is intended to provide fixation during the healing process following a syndesmotic trauma.

The Synde-Lock™ Syndesmosis Repair Kit contains an implant designed as two bone anchors, one a stainless steel screw and the other a PEEK T-nut, connected by a distribution bridge comprised of UHMWPE suture and a threaded PEEK rod. The stainless steel screw is pre-threaded with the UHMWPE suture section of the distribution bridge. The PEEK T-nut engages the threads of the PEEK section of the distribution bridge allowing the effective length of the bridge to be shortened. A clipper is also contained in the kit to cut away excess distribution bridge material, once the implant is in place. The Tarsus Synde-Lock Syndesmosis Repair Kit is intended to provide fixation during the healing process following a syndesmotic trauma.

The provided text describes a medical device, the "Synde-Lock™ Syndesmosis Repair Kit," and its approval process, but it does not include detailed acceptance criteria or a dedicated study report proving the device meets specific performance metrics in the way typically seen for AI/ML-based medical devices.

Instead, the submission focuses on demonstrating substantial equivalence to a predicate device for a traditional medical implant. The "acceptance criteria" here are implicitly built into the substantial equivalence paradigm (i.e., the new device performs at least as well as the predicate for its intended use).

Here's an analysis based on the provided text, structured to address your points as much as possible, while acknowledging the limitations of the input:

1. Table of Acceptance Criteria and Reported Device Performance

As noted, explicit, quantitative acceptance criteria (e.g., "sensitivity must be > 90%") are not detailed in this submission. The "acceptance criteria" for this traditional medical device are met by demonstrating substantial equivalence to a legally marketed predicate device. The "reported device performance" is framed in comparison to that predicate.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not explicitly stated as a numerical value within the provided text. The text mentions "Both synthetic bone analog and cadaveric bone were used for testing." This implies multiple test samples of each type were used, but the exact count is not given.
• Data Provenance: The testing was "performed by the company" (Tarsus Medical, Inc.). It's non-clinical/pre-clinical testing. The text does not specify the country of origin for the cadaveric bone, nor does it classify the study as retrospective or prospective (which typically applies to clinical patient data, not mechanical testing).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

• This question is not applicable in the context of this submission. The "ground truth" for this device is not established by human experts interpreting data (like in an AI/ML study). Instead, it's established by the physical testing and engineering principles to demonstrate mechanical performance compared to a predicate device.
• The comparison is to the predicate device's established performance, and the assessment of "substantial equivalence" would be made by FDA reviewers, who are experts in regulatory compliance and device mechanics, but they are not establishing a "ground truth" for the device's technical performance in this manner.

4. Adjudication Method for the Test Set

• This question is not applicable. There's no human interpretation or "adjudication" of results in the sense of expert consensus for mechanical testing data. The results are quantitative measurements against defined test protocols. The "adjudication" process is the FDA's regulatory review of the company's test reports and their claim of substantial equivalence.

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

• This question is not applicable. This is a traditional mechanical implant, not an AI/ML-driven device or an imaging interpretation tool. Therefore, no MRMC study or assessment of human reader improvement with AI assistance was performed.

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

• This question is not applicable. This is a physical medical implant, not an algorithm.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• The "ground truth" for this device's performance demonstration relies on biocompatibility standards (ASTM, USP for materials) and established mechanical testing methodologies to simulate physiological conditions and compare against the predicate device. It's not based on expert consensus, pathology, or outcomes data in the way an AI diagnostic tool would be. The "truth" is whether the device meets the mechanical demands for its intended use and performs comparably to a proven device.

8. The Sample Size for the Training Set

• This question is not applicable. This is a traditional medical device, not an AI/ML system, so there is no "training set."

9. How the Ground Truth for the Training Set was Established

• This question is not applicable for the reasons stated above.

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