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The aap LOQTEQ® Clavicle Shaft Plate and aap LOQTEQ® Superior Lateral Clavicle Plate are intended for fixation of fractures, malunions, non-unions, and osteotomies of the clavicle.

The aap LOQTEQ® AcroPlate is intended for fixation of lateral clavicle fractures and dislocations of the acromioclavicular joint.

The aap LOQTEQ® Clavicle Plates 2.7/3.5 consists of bone plates and bone screws, to be implanted by a surgeon in order to achieve an internal fixation of bone fragments typically after fractures, osteotomies. If the plates are used in conjunction with locking screws, a so called internal fixator will be realized (internal fixation).
The aap LOQTEQ® Clavicle Plates 2.7/3.5 incorporate:
• LOQTEQ® Clavicle Shaft Plate (left and right)
• LOQTEQ® Superior Lateral Clavicle Plate (left and right)
• LOQTEQ® AcroPlate 3.5 (left and right)
to be used with
• LOQTEQ® Cortical Screw 3.5, small head, T15, self-tapping
• LOQTEQ® Cortical Screw 2.7, small head, T8, self-tapping
• Cortical Screw 2.5, small head,T8, self-tapping
• Cortical Screw 3.5, small head, self tapping

• Set of Instruments aap LOQTEQ® Clavicle Plates 2.7/3.5

The provided text describes a medical device, the "aap LOQTEQ® Clavicle Plates 2.7/3.5", which is a bone fixation appliance. The document is a 510(k) premarket notification summary from the FDA. It details the device's intended use, materials, and a comparison to predicate devices, supported by non-clinical performance data.

However, the provided text does not include information about AI/ML device performance, ground truth establishment for a test set, sample sizes for training or test sets in the context of AI, multi-reader multi-case studies, or standalone algorithm performance. The device is a traditional metallic bone fixation appliance, not an AI or imaging diagnostic tool.

Therefore, many of the requested categories related to AI modeling are not applicable to this document. I will focus on the acceptance criteria and study information that is present for this traditional medical device.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test set sample size: Not explicitly stated in terms of number of plates or tests. The document refers to "customized 4-point-bending tests" and "test results gained," implying multiple tests were performed.
• Data provenance: Not specified in terms of country of origin. The test data is "Non-clinical," implying laboratory testing rather than human subject data. Retrospective or prospective is not applicable for non-clinical mechanical testing.

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

This question is not applicable. The "ground truth" for this device's performance is objective mechanical testing (e.g., load-to-failure), not expert interpretation of outputs like in AI/imaging devices.

4. Adjudication method for the test set

This question is not applicable. Adjudication methods like 2+1 or 3+1 typically refer to expert consensus processes for labeling ground truth in diagnostic studies, which is not relevant here. Mechanical tests have objective outcomes.

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. The device is a physical implant for bone fixation, not an AI or diagnostic imaging tool. MRMC studies are used for evaluating diagnostic performance with human readers.

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

This question is not applicable. The device is a physical implant, not an algorithm.

7. The type of ground truth used

The "ground truth" for this device's performance evaluation was based on objective mechanical testing outcomes from customized 4-point-bending tests according to ASTM F382-99, demonstrating strength, durability, and resistance to deformation under load. The ground truth for proving substantial equivalence was the performance of the predicate devices under similar testing conditions.

8. The sample size for the training set

This question is not applicable. This is a traditional mechanical device, not an AI model requiring a "training set."

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

This question is not applicable, as there is no "training set" for an AI model. For the mechanical testing, the "ground truth" (i.e., the expected performance benchmarks) was established by the performance characteristics of the predicate devices and the requirements of the ASTM F382-99 standard.

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