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The system is intended for use in adult and pediation: transitional adolescent B (18 years to

The G3TM Active Plate® Large Fragment System is a straight plate and locking screw system comprised of a variety of sizes to accommodate various patient anatomy and pathology. The plates and screws are intended to be used for long bone fracture fixation. All implantable components are manufactured from medical grade titanium alloy (Ti6Al4V-ELI) and silicone elastomer in the subject device. The screws are 5.0 mm diameter and come in lengths ranging from 14 to 145 mm. The plates range in size from 6 holes to 14 holes. The plates incorporate sliding elements, which are constrained within the plate and embedded in an elastomer sheath that is bonded to the sliding element. Once locking screws are inserted, the active elements allow for independent controlled axial translation of the screws. All instruments are made from stainless steel.

The provided document is a 510(k) summary for the G3™ Active Plate® Large Fragment System, which is a bone fixation device. It is a submission to the FDA for market clearance, not a study evaluating device performance against pre-defined acceptance criteria in the way one would for a diagnostic or AI-driven device.

Therefore, the information typically found in acceptance criteria and study designs for such devices (like accuracy, sensitivity, specificity, sample size, ground truth establishment, expert qualifications, etc.) is not applicable to this submission.

This submission focuses on demonstrating substantial equivalence to a previously cleared predicate device, the G3™ Active Plate Large Fragment System (K142938). The primary argument for substantial equivalence rests on the fact that the subject device is "identical in every way except for the process by which the sliding element is manufactured."

Instead of a typical performance study, the submission references mechanical testing that was previously performed on the predicate device. The underlying assumption is that since the only change is a manufacturing process for a sub-component, and the form, fit, and intended function remain the same, the previous mechanical testing results are still valid and applicable.

Here's a breakdown of what can be extracted, even though it doesn't align with the requested format for a diagnostic/AI device:

1. A table of acceptance criteria and the reported device performance

• Acceptance Criteria: While not explicitly stated as "acceptance criteria" in the context of a new performance study, the underlying criteria for substantial equivalence are that the subject device performs at least as well as the predicate device in terms of mechanical properties and safety.

• Reported Device Performance:

  • Performance Tests Performed (on predicate device): Stiffness, fatigue, pullout, torsion, torque, construct fatigue, and wear.
  • Standards Referenced: ASTM F543 and ASTM F382.
  • Conclusion: The submission asserts that "Since the subject and predicate are identical in every way except for the process by which the sliding element is manufactured, this testing also applies to the subject device and thus the subject device is substantially equivalent to the predicate."

  Table (Re-interpreted for this context):

  Performance Metric (Tested on Predicate)	Acceptance Criterion (Implicit for Substantial Equivalence)	Reported Performance (Inferred for Subject Device)
  Stiffness	Meets ASTM F543/F382 standards and performs equivalently to predicate.	Deemed equivalent to predicate, meeting standards.
  Fatigue	Meets ASTM F543/F382 standards and performs equivalently to predicate.	Deemed equivalent to predicate, meeting standards.
  Pullout	Meets ASTM F543/F382 standards and performs equivalently to predicate.	Deemed equivalent to predicate, meeting standards.
  Torsion	Meets ASTM F543/F382 standards and performs equivalently to predicate.	Deemed equivalent to predicate, meeting standards.
  Torque	Meets ASTM F543/F382 standards and performs equivalently to predicate.	Deemed equivalent to predicate, meeting standards.
  Construct Fatigue	Meets ASTM F543/F382 standards and performs equivalently to predicate.	Deemed equivalent to predicate, meeting standards.
  Wear	Meets ASTM F543/F382 standards and performs equivalently to predicate.	Deemed equivalent to predicate, meeting standards.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• This information is not provided. The performance data discussed refers to mechanical testing of physical device components, not a clinical test set of patient data.

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)

• Not applicable. Ground truth in this context would refer to material properties and mechanical performance, which are established through standardized engineering tests, not expert human review.

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

• Not applicable 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

• Not applicable. This is not an AI-driven device or diagnostic tool.

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

• Not applicable. This is not an algorithm.

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

• The "ground truth" for the mechanical testing would be defined by the specifications and measurement results obtained from applying the ASTM F543 and ASTM F382 standards to the physical devices.

8. The sample size for the training set

• Not applicable. This is a physical device, not a machine learning model.

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

• Not applicable.

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The system is intended for use in adult and pediatric (subpopulation: transitional adolescent B (18 years to

The G3 ™ Active Plate ® Large Fragment System is a straight plate and locking screw system comprised of a variety of sizes to accommodate various patient anatomy and pathology. The plates and screws are intended to be used for long bone fracture fixation. All implantable components are manufactured from medical grade titanium alloy (Ti6Al4V-ELI) and silicone elastomer in the subject device. The screws are 5.0 mm diameter and come in lengths ranging from 14 to 145 mm. The plates range in size from 6 holes to 14 holes. The plates incorporate sliding elements, which are constrained within the plate and embedded in a silicone elastomer sheath that is bonded to both the plate and sliding element. Once locking screws are inserted, the active elements allow for independent controlled axial translation of the screws. All instruments are made from stainless steel.

The provided text describes a 510(k) premarket notification for the G3™ Active Plate® Large Fragment System, a medical device for bone fixation. However, it does not contain the specific detailed information typically found in a study proving the device meets particular acceptance criteria, especially for AI/ML device performance.

The document primarily focuses on establishing substantial equivalence to predicate devices based on intended use, material, geometry, and mechanical testing (stiffness, fatigue, pullout, torsion, torque, construct fatigue, and wear) according to ASTM F543 and ASTM F382. It also notes biocompatibility evaluation per ISO 10993-1.

Therefore, many of the requested elements for an AI/ML device's acceptance criteria and study cannot be extracted from this document, as it outlines a submission for a traditional mechanical medical device, not an AI/ML product.

I will fill in the table and address the questions to the best of my ability based on the provided text, indicating where information is not present.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document describes mechanical performance testing to demonstrate substantial equivalence, rather than traditional "acceptance criteria" with specific thresholds for sensitivity, specificity, or other AI/ML metrics. The performance is assessed against predicate devices.

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

• Sample Size for Test Set: Not specified. The document mentions "mechanical testing" and "construct fatigue testing" per ASTM standards, but does not provide details on the number of samples or specimens used for these tests.
• Data Provenance: Not applicable in the context of clinical data. The tests are in vitro mechanical tests.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Not applicable. This device is a mechanical implant, not an AI/ML diagnostic or image analysis device that would require expert-established ground truth from clinical data. The "ground truth" for mechanical testing is derived from standardized test methods and measurement equipment.

4. Adjudication method for the test set

• Not applicable. Adjudication methods are relevant for expert review of clinical data, not for in vitro 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 is a mechanical device, not an AI/ML diagnostic tool.

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

• Not applicable. This is a mechanical device, not an AI/ML algorithm.

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

• The "ground truth" for the performance testing is based on standardized mechanical testing methodologies (e.g., ASTM F543, ASTM F382) which provide quantitative measurements of properties like stiffness, fatigue life, pullout strength, etc. The performance is then compared against established predicate devices.

8. The sample size for the training set

• Not applicable. This document is for a mechanical device, not an AI/ML device that requires a training set of data.

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

• Not applicable. This document is for a mechanical device, not an AI/ML device.

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