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The aap LOQTEQ® Distal Medial Tibia Plate 3.5 System is intended for

• Fixation of complex intra- and extra- articular fractures of the distal tibia
• Osteotomies of the distal tibia

Bone plates and screws are used for fixation of bone fragments, i.e., for treatment of bone fractures and other bone injuries. Bone plates are fixed by the use of bone screws. Bone plates and bone screws are implants. If the plates are used in conjunction with locking screws, a so called internal fixator (internal fixation) will be realized.

The LOQTEQ® Distal Medial Tibia Plate 3.5 System consists of:
• LOQTEQ® Distal Medial Tibia Plate 3.5, left and right version
• LOQTEQ® Cortical Screws 3.5, self-tapping (locking bone screw)
• LOQTEQ® Cortical Screws 3.5 small head, self-tapping (locking bone screw)
• Cortical Screws 3.5, self-tapping
• Cortical Screws 3.5 small head, self tapping
• Instruments, Distal Medial Tibia Plate 3.5

The provided text describes a medical device, the "aap LOQTEQ® Distal Medial Tibia Plate 3.5 System," and its non-clinical performance evaluation, specifically focusing on mechanical testing for substantial equivalence.

Here's an analysis based on your requested information:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document only states "Fatigue implant tests with progressive loadings, representing worst case scenario with respect to clinical use." It does not specify:

• The exact sample size for the mechanical tests.
• The data provenance (e.g., country of origin, retrospective or prospective). Mechanical tests are typically performed in a lab setting, so "provenance" in the context of patient data is not applicable here.

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

This information is not provided in the document. The "ground truth" for mechanical testing is typically established by engineering standards and test protocols, not by expert medical consensus in the same way as, for example, image interpretation.

4. Adjudication method for the test set

This information is not provided in the document. Adjudication methods like 2+1 or 3+1 are relevant for studies involving human interpretation or clinical endpoints, not typically for mechanical bench 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, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted. This document describes the mechanical testing of a bone plate system, not an AI-powered diagnostic device that would involve human readers or AI assistance in interpretation.

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

This question is not applicable as the device is a medical implant (bone plate system), not a software algorithm.

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

For the non-clinical performance data (mechanical tests), the "ground truth" is established by engineering standards and established biomechanical principles for measuring strength, fatigue life, and other mechanical properties under simulated physiological conditions. The document refers to "pre-defined acceptance criteria," which would stem from these engineering standards and comparison to the predicate device.

8. The sample size for the training set

This information is not provided and is not applicable to this type of device (mechanical implant). The concept of a "training set" is relevant for machine learning algorithms, not for the mechanical testing described.

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

This information is not provided and is not applicable as there is no "training set" in the context of this device's evaluation.

In summary: The provided document is a 510(k) summary for a medical implant, focusing on demonstrating substantial equivalence to a predicate device primarily through non-clinical mechanical testing. It does not involve AI, human readers, or clinical trials with patient-specific ground truth in the way a diagnostic AI device would. Therefore, many of your questions are not relevant or addressed by this type of submission.

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