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The Smith & Nephew ENDOBUTTON Continuous Loop is used for fixation of tendons and Iigaments during orthopedic reconstruction procedures such as Anterior Cruciate Ligament (ACL) reconstruction and acromioclavicular joint separations due to coracoclavicular ligament disruptions.

The ENDOBUTTON CL is a machined titanium implant assembled with a continuous loop of polyester tape. It is oblong in shape with four holes through which suture is threaded. The polyester loop is preattached to the center holes. The device is designed to provide cortical fixation in the repair of tendons and ligaments. The design of the ENDOBUTTON allows for the device to be endoscopically delivered from a single access point. The device is available with premeasured loops of tape ranging from 20mm-50mm to accommodate different graft sizes.

The ENDOBUTTON is a machined titanium implant designed to provide cortical fixation in the repair of tendons and ligaments. It is oblong in shape with four holes through which suture is threaded. The design of the ENDOBUTTON allows for the device to be endoscopically delivered from a single access point.

This document describes the Endobutton Continuous Loop (CL), a suture retention device used for fixation of tendons and ligaments. The submission is for an indication expansion for this existing device, not for a new device requiring extensive performance testing for novel functionality. Therefore, the "study that proves the device meets the acceptance criteria" in this context refers to demonstrating substantial equivalence for the expanded indications to a legally marketed predicate device through performance testing.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state specific quantitative acceptance criteria (e.g., "tear strength must be X N"). Instead, the acceptance criterion for this 510(k) submission is substantial equivalence to a predicate device for the expanded indications.

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

The document does not provide details on:

• The specific sample size (N-number) used for any mechanical or performance testing.
• The explicit data provenance (e.g., country of origin, retrospective or prospective) of the performance testing data.

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

This type of information is not applicable to this 510(k) submission. "Ground truth" for medical decisions or diagnoses made by experts is typically relevant for AI/ML or diagnostic device submissions where human interpretation is being evaluated or augmented. For a mechanical fixation device, validation relies on engineering and biomechanical testing, not expert consensus on medical images or patient outcomes in the same way.

4. Adjudication Method for the Test Set

This is not applicable for the reasons stated in point 3.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

This is not applicable. An MRMC study is relevant for diagnostic devices where multiple readers interpret cases. This submission is for a surgical implant (fixation device), not a diagnostic tool requiring human interpretation.

6. Standalone Performance Study (Algorithm Only Without Human-in-the-Loop Performance)

This is not applicable. This device is a physical surgical implant, not an algorithm or software. Its "performance" is assessed through biomechanical testing, not algorithmic output.

7. Type of Ground Truth Used

Ground truth in this context would implicitly be derived from biomechanical testing standards and engineering principles that simulate the forces and stresses the device would experience in vivo. The performance is compared to the predicate device to show similar mechanical properties for the expanded indications. The document implies performance testing, which would involve quantifiable physical measurements.

8. Sample Size for the Training Set

This is not applicable. There is no "training set" as this is a physical medical device, not a machine learning model.

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

This is not applicable for the reasons stated in point 8.

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