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The Arthrex Patella SuturePlates are intended for use in stabilization of patella fractures.

The Arthrex Patella SuturePlates are manufactured from titanium alloy conforming to ASTM F136. The plates are 1.6 mm thick in a semi-contoured, mesh-like design. The plates are available in Arrow, Star and Star Pole configurations. Each plate provides locking screw fixation along with suture holes for fixation of the surrounding soft tissue to the plate. The Arthrex Patella SuturePlates are intended to be used with existing FDA cleared Arthrex screws. The Arthrex Patella SuturePlates are sold sterile and non-sterile and are single use.

The acceptance criteria and study detailed in the provided document pertain to the Arthrex Patella SuturePlates, a medical device, not an AI/ML algorithm. Therefore, many of the typical acceptance criteria and study aspects related to AI/ML device performance (like sample sizes for test/training sets, data provenance, expert ground truth, adjudication methods, MRMC studies, standalone performance, etc.) are not applicable here.

This document describes the premarket notification for a Class II metallic bone fixation appliance, and the studies focus on demonstrating its substantial equivalence to previously cleared predicate devices through biomechanical, MRI safety, and biocompatibility testing. The "performance" being evaluated is essentially the physical and biological compatibility of the device.

However, I will extract the relevant information that is present in the document.

Acceptance Criteria and Reported Device Performance for Arthrex Patella SuturePlates

Since this is a physical medical device and not an AI/ML system, the "acceptance criteria" are related to established engineering and biological standards and comparison to a predicate device, rather than diagnostic performance metrics.

1. Table of Acceptance Criteria and the Reported Device Performance:

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

• Sample Size: Not explicitly stated in terms of number of devices or test repetitions for each specific test, other than implying sufficient samples were tested to demonstrate statistical equivalence for biomechanical tests.
• Data Provenance: Not applicable as this relates to physical device testing, not patient data. The tests are laboratory-based.

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

• Not applicable as this is a physical device submission; ground truth is established through standardized testing procedures and engineering/biological principles, not expert interpretation of data.

4. Adjudication method for the test set:

• Not applicable. Performance is determined by meeting pre-defined test standards and demonstrating statistical equivalence to predicate devices, not through expert adjudication.

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/ML device that assists human readers.

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

• Not applicable. This is not an AI/ML algorithm.

7. The type of ground truth used:

• For Biomechanical Testing: Ground truth is established by mechanical engineering principles, established ASTM standards (e.g., ASTM F382), and comparative performance against the predicate device.
• For MRI Safety: Ground truth is established by established ASTM standards (e.g., F2052, F2119, F2182, F2213) and FDA guidance for MRI environments.
• For Pyrogenicity: Ground truth is established by biological testing standards (e.g., ANSI/AAMI ST72:2011/(R)2016, USP , USP , EP 2.6.14) that define acceptable pyrogen limits.
• For Biocompatibility: Ground truth is established by international standard ISO 10993-1:2018 "Biological evaluation of medical devices".

8. The sample size for the training set:

• Not applicable. This is a physical device, not an AI/ML algorithm requiring a training set.

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

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

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The Arthrex Mesh Plates are intended for use in stabilization of fresh fractures, revision procedures, joint fusion and reconstruction of small bone fragments of the hand/wrist, foot/ankle, osteopenic bone and patella fractures.

The Arthrex Mesh Plates are manufactured from either titanium alloy or stainless steel. The plates are 1.3 mm thick in a semi-contoured, mesh-like design. The plates are available in long and short versions ranging from 50 mm to 115 mm in length. The plates are designed for the surgeon to cut to a desired length as needed. The Arthrex Mesh Plates are intended to be used with existing FDA cleared Arthrex screws. The Arthrex Mesh Plates are sold sterile and non-sterile and are single use.

The document describes the acceptance criteria and supporting studies for the Arthrex Mesh Plates, a medical device, not an AI/ML product. Therefore, several points in your request related to AI/ML devices (e.g., sample size for the test set, number of experts, adjudication method, MRMC study, standalone performance, training set details) are not applicable to this submission.

Here's a breakdown of the available information regarding the acceptance criteria and studies for the Arthrex Mesh Plates:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Not Applicable. This information is typically relevant for AI/ML device studies. This submission pertains to a physical medical device (bone fixation plates). The mechanical and other tests would have specific sample sizes as per relevant ASTM/ISO standards, but these details are not provided in this summary.

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

• Not Applicable. Ground truth, in the context of expert review, is typically relevant for AI/ML devices where human expert judgment is used to label data. For a physical device, performance is evaluated against engineering specifications and regulatory standards.

4. Adjudication method for the test set

• Not Applicable. Adjudication methods are typically used in clinical studies or for setting ground truth in AI/ML performance evaluation based on expert consensus.

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. MRMC studies are specific to AI/ML devices that assist human readers (e.g., radiologists interpreting images). This device is a surgical implant.

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

• Not Applicable. This refers to AI/ML algorithm performance without human intervention, which is not relevant for a physical implantable device.

7. The type of ground truth used

• The "ground truth" for this device's performance is established by engineering standards, regulatory guidelines (e.g., FDA guidance for MR safety, ISO 10993 for biocompatibility), and direct mechanical testing against predicate device performance. It's not based on expert consensus, pathology, or outcomes data in the way an AI/ML diagnostic device might be. The goal is to demonstrate equivalence to a legally marketed predicate device.

8. The sample size for the training set

• Not Applicable. "Training set" is a concept specific to AI/ML model development.

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

• Not Applicable. This is specific to AI/ML model development.

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