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Medline UNITE® Calcaneal Fracture Plating System are intended for use in stabilization of fresh fractures, revision procedures, joint fusion, and reconstruction of bones in the feet and ankles including extra-articular, joint depression, tongue-type, and severely comminuted fracture of the calcaneus. The system can be used both in adult and pediatric (adolescent and child) patients.

The Medline UNITE® Calcaneal Fracture Plating System consists of 3 styles of Sinus Tarsi plates (Sinus Tarsi, Sinus Tarsi Offset, and Sinus Tarsi Extension) made from Titanium Alloy (Ti-6Al-4V ELI) and Perimeter plates made from Commercially Pure Titanium. The Sinus Tarsi Offset, and Sinus Tarsi Extension, and Perimeter plates are each offered in small, medium, and large sizes in left and right configurations. The plates can accommodate 2.7mm, 3.5mm and 4.0mm screws. Previously cleared 3.5mm locking and non-locking screws (K151235) that range from 10-60mm in length will be included in the tray with the Medline UNITE® Calcaneal Fracture Plates. The system also includes reusable instrumentation necessary to implant the plates and screws, e.g. targeting guide, drill bits

This document is a 510(k) premarket notification for the Medline UNITE® Calcaneal Fracture Plating System. It focuses on demonstrating substantial equivalence to a predicate device, not on proving device performance against acceptance criteria through a study involving AI. Therefore, the requested information elements related to AI/algorithm performance and clinical study outcomes cannot be extracted from this document.

The document primarily describes a medical device, its indications for use, and a comparison of its technological characteristics to a predicate device. The performance testing outlined is largely bench testing to demonstrate mechanical equivalence, not clinical effectiveness studies.

Here's what can be extracted based on the provided document:

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

The document does not provide explicit acceptance criteria in a quantitative sense for performance metrics like sensitivity, specificity, or image quality, as it's not and AI device undergoing such validation. Instead, the "acceptance criteria" are implied by the substantial equivalence determination process, where the device must perform equivalently to a legally marketed predicate device.

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

Not applicable. The "test set" in this context refers to physical devices undergoing mechanical testing, not a dataset for an AI algorithm. The document mentions "Single Cycle 4-Point Bend Testing" and "Bending Fatigue Testing," which involve physical samples of the plates. The document does not specify the exact number of plates tested in these bench tests, nor does it specify data provenance as it would for a clinical study with patient data.

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

Not applicable. Ground truth for AI algorithms is not relevant here as this is a physical medical device. The "ground truth" for the performance tests is derived from standardized mechanical testing protocols (ASTM F382).

4. Adjudication method for the test set

Not applicable. Adjudication methods are typically for clinical studies where expert consensus is needed. For mechanical testing, the results are objectively measured per established ASTM standards.

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. This is not an AI device, and therefore, an MRMC study involving human readers and AI assistance was not performed. The document explicitly states: "Performance Testing (Clinical) This section does not apply. No clinical testing was performed." and "Summary of Clinical Testing Not applicable."

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

No. This is not an AI device.

7. The type of ground truth used

For the bench testing, the "ground truth" or reference for comparison is the performance of the legally marketed predicate device as measured by the specified ASTM standards.

8. The sample size for the training set

Not applicable. There is no AI algorithm, so there is no training set.

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

Not applicable. There is no AI algorithm.

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The Biomet A.L.P.S Calcaneal Plating System is intended for fixation of fractures, fusions, and osteotomies of the calcaneus.

The new A.L.P.S calcaneal plating system is a series of bone plates and screws intended to stabilize calcaneal fractures. The plates come in a left and right specific MIS configuration with five size options and left and right MESH configuration that is offered in three sizes. The plates are manufactured from titanium (ASTM F136) and will utilize the legally marketed and predicate A.L.P.S screw system.

Here's an analysis of the provided text regarding the A.L.P.S Calcaneal Plating System.

Note: This submission pertains to a medical device (bone fixation plates) and not an AI/software device. Therefore, many of the requested elements that are typical for AI/software device studies (like expert adjudication, MRMC studies, ground truth for training sets, etc.) are not applicable or addressable from the provided text. The evaluation criteria for such a device are primarily focused on mechanical performance and material equivalence.

Acceptance Criteria and Device Performance Study for the A.L.P.S Calcaneal Plating System

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: Not explicitly stated in the provided text. The studies were "preclinical performance tests/rationales" and engineering rationales. For medical device mechanical testing, sample sizes are typically determined by statistical power analysis for a given test (e.g., fatigue, static strength), but the specific numbers are not disclosed here.
• Data Provenance: The studies are described as "preclinical performance tests" and "engineering rationales". This implies laboratory-based mechanical testing and engineering analysis. No country of origin is specified for the data itself, but the submitter is Biomet Manufacturing Corp. in Warsaw, IN, USA. The tests are retrospective in the sense that they are performed on manufactured components separate from a patient population, but the results are for substantiating a new device.

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

• Not Applicable. This is a mechanical device, not an image analysis or diagnostic AI device. "Ground truth" in this context refers to the physical properties and performance of the device under testing conditions, measured by instruments, not by expert human interpretation.

4. Adjudication method for the test set:

• Not Applicable. As a mechanical device, adjudication in the sense of reconciling expert opinions is not relevant. Mechanical test results are objective measurements.

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/software device and therefore an MRMC study is not relevant.

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

• Not Applicable. This is not an AI/software device. The device's "performance" is its mechanical integrity and function as a standalone implant. The preclinical tests evaluated the device in isolation, in a lab setting, independent of human interaction during the test itself.

7. The type of ground truth used:

• Objective Mechanical Measurements and Engineering Analysis: The "ground truth" for this device's performance is derived from standardized mechanical testing (e.g., fatigue testing, static load testing, material property verification) to ASTM or ISO standards, as well as engineering rationales and comparisons against predicate device performance under similar conditions. This would involve quantifiable measurements of force, displacement, cycles to failure, and material properties.

8. The sample size for the training set:

• Not Applicable. This is not an AI/software device and does not have a "training set" in that context. The device's design and manufacturing processes are developed through engineering principles, rather than machine learning 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/software, this question does not apply. The "ground truth" for the device's design and manufacturing is established through engineering specifications, material standards (e.g., ASTM F136 for titanium), and quality control processes.

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