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The Bone Anchor System and Kit is indicated for securing soft tissue structures to bone anchors in surgical procedures where bone anchors are indicated. Examples of these procedures include:

The BAK is indicated for use in both traditional open surgery and endoscopic surgical procedures.

The Axya Bone Anchor System consists of an electronic control module and a reusable welding activator with an integral connector cable. The Kit (BAK) consists of a disposable suture welding sleeve packaged together with appropriate and currently approved bone anchors and USP polypropylene monofilament suture. The Kit is intended for use with the reusable system components in order to complete the suture welding process for the attachment of soft tissues to bone anchors in surgical procedures where bone anchors are indicated

The Axya Bone Anchor System and Kit (BAK) is a Class II device intended to secure soft tissues to bone anchors in surgical procedures. The device uses an ultrasonic suture welding process.

Here's an analysis of the acceptance criteria and supporting study information:

1. Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state specific numerical acceptance criteria for the device's performance. Instead, it focuses on demonstrating that the BAK is substantially equivalent to predicate devices and that its performance (specifically suture strength and safety) meets established standards and is comparable to traditional methods.

The reported device performance is presented as:

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

The document describes two main types of studies:

• In Vitro (Bench-test) Studies:

  • Sample Size: Not explicitly stated with specific numbers for each test (e.g., number of sutures tested for knot strength, number of samples for fatigue). It generally refers to "data presented" and "tests" covering "respective sizes and types of suture materials" and "USP size 2 polypropylene monofilament sutures."
  • Data Provenance: Conducted "in vitro" and described as "bench-test models." No country of origin is specified, but it's implied to be part of the manufacturer's testing in the US. The data is retrospective in the sense that it refers back to studies conducted for prior 510(k) clearances (ASLS and SWK).

• Animal Studies:

  • Sample Size: Not explicitly stated as a number of animals or implant sites. It mentions "New Zealand rabbits" for dorsal skin and bowel tissue studies. Without specific numbers, it's impossible to quantify the sample size.
  • Data Provenance: Animal models (New Zealand rabbits). The studies were previously conducted and referenced from a prior 510(k) (K980988), making them retrospective in the context of this current submission.

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

This information is not provided in the document.

• For the in vitro studies, "ground truth" would be objective measurements (e.g., force required for failure, cycle count to failure), not expert consensus.
• For the animal studies, the "investigators" concluded on safety and efficacy based on histopathology. However, the number of investigators/pathologists, their specific qualifications, or how their consensus was reached is not mentioned.

4. Adjudication Method

This information is not provided in the document. Given the nature of the studies (in vitro measurements and animal histopathology from earlier submissions), a formal human-reader adjudication method as often seen in imaging studies (e.g., 2+1) is unlikely to apply directly. The "investigators" formed conclusions, but the method of their consensus/adjudication is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study was not done.

This type of study is primarily relevant for diagnostic imaging devices where human readers interpret medical images with and without AI assistance to assess changes in diagnostic accuracy or efficiency. The Axya Bone Anchor System is a surgical tool, not a diagnostic imaging device. The comparative effectiveness assessment focused on mechanical properties (suture strength) and biological safety (tissue reaction) compared to traditional methods, not human reader performance.

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

Yes, in essence, standalone performance was evaluated for the device's core function.

The in vitro studies on suture strength, fatigue, and elongation represent the standalone mechanical performance of the welded suture. The animal safety studies evaluated the biological response to the device's output (welded suture) without direct human intervention impacting the "welding" function itself during the test. The "algorithm" here isn't a software algorithm but rather the mechanical and ultrasonic welding process. The efficacy of this process was demonstrated independently of varied human surgical technique (e.g., how a human would tie a knot).

7. The Type of Ground Truth Used

• Quantitative Bench-Test Data: For suture strength and fatigue, the ground truth was derived from objective, measurable outcomes obtained from bench-tests (e.g., force measurements for knot strength, cycle counts for fatigue). These are "physical performance metrics."
• Histopathology: For the animal safety studies, the ground truth for tissue reaction and safety was established by detailed microscopic examination of tissue samples (histopathology) at the implant sites. This is a "pathology" based ground truth.

8. The Sample Size for the Training Set

Not applicable.

The Axya Bone Anchor System is a mechanical device, not a machine learning or AI-driven system that requires a "training set" in the conventional sense. The device's function is deterministic based on its design and physical principles. Its development would involve engineering design, prototyping, and testing, not statistical training on a dataset.

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

Not applicable.

As there is no training set as understood in AI/ML, there is no ground truth established for a training set. The device's performance is demonstrated through its inherent mechanical and biological properties, not learned patterns from data.

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