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The indications for the Emerge Medical Small Fragment Locked Plating System are as follows for the two subsystems:

The Emerge Medical Locking Medial Distal Tibia Plate Set is indicated for the fixation of fractures of the distal tibia including, but not limited to, ankle fractures, periarticular, intraarticular and distal tibia fractures with a shaft extension, malleolar and distal fibular fractures.

The Emerge Medical Locking Proximal Tibia Plate Set is intended for treatment of non-unions, malunions, and fractures of the proximal tibia, including simple, comminuted, lateral wedge, depression, medial wedge, bicondylar, combinations of lateral wedge and depression, and fractures with associated shaft fractures.

The Emerge Medical Small Fragment Locked Plating System Line Extension is not intended for use with active or latent infection, osteoporosis, insufficient quantity of bone/soft tissue, material sensitivity (if suspected tests should be performed prior to implantation), sepsis, and patients who are unwilling or incapable of following postoperative care instructions. This device is not intended for screw attachment or fixation to the posterior elements (pedicles) of the cervical, thoracic, or lumbar spine.

The Emerge Medical Small Fragment Locked Plating System Line Extension consists of implants and instruments designed to be used for internal bone alignment and fixation of fractures of the tibia. The system features a single plate design, bone screws for fixation, and a set of instruments to facilitate installation and removal of the implants. The plates have screw holes, which allow for attachment to the bones or bone fragments. The plates are fabricated from medical grade stainless steel (ASTM F139-12), and offered in various widths, lengths, and thicknesses. Plates and screws are provided non-sterile.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text, specifically focusing on what is not present in the document for AI/software-based devices:

It's important to note that the provided document describes a physical medical device (a bone fixation system), not an AI/software-based device. Therefore, many of the typical criteria and study types associated with AI device validation (like expert consensus, radiologists, MRMC studies, training/test sets, ground truth establishment) are not applicable and are consequently absent from this submission.

Acceptance Criteria and Reported Device Performance

The core acceptance criterion for this type of medical device (a substantial equivalence determination for a 510(k) submission) is demonstrating that it is as safe and effective as a legally marketed predicate device. This is primarily achieved through showing similar technological characteristics and equivalent performance.

Study Details (Relevant to Physical Device Validation - Not AI/Software)

As this is not an AI/software device, the questions related to AI-specific validation (sample size for test/training sets, data provenance, number of experts for ground truth, adjudication, MRMC, standalone performance, type of ground truth) are not directly applicable.

Instead, the study conducted for this device is a non-clinical performance evaluation:

1. Sample size used for the test set and the data provenance: Not applicable in the context of an AI device. For this physical device, the evaluation was primarily through finite element analysis (FEA), which is a computational modeling technique. There isn't a "test set" in the sense of patient data. The "data provenance" would refer to the engineering and materials specifications used in the FEA.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for a physical device's mechanical properties is established through engineering standards, material science, and computational modeling principles, not clinical expert consensus on patient data.
3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
4. 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, as this is not an AI-assisted diagnostic or prognostic device.
5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): For this physical device, the "ground truth" for evaluating performance would be engineering and biomechanical principles, material properties (ASTM F139-12 for stainless steel), and comparison to established predicate device performance. The FEA would be validated against these physical principles and potentially prior physical testing data for similar devices to ensure its accuracy.
7. The sample size for the training set: Not applicable for a physical device validated via FEA.
8. How the ground truth for the training set was established: Not applicable.

Summary for AI-Specific Questions (based on device type):

• 1. Sample sized used for the test set and the data provenance: Not applicable. The "study" was Finite Element Analysis (FEA) of the device's mechanical properties.
• 2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
• 3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
• 4. 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, not done. This is a physical bone fixation device.
• 5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No, not done. This is a physical bone fixation device.
• 6. The type of ground truth used (expert concensus, pathology, outcomes data, etc): Engineering and biomechanical principles, material properties, and performance data from predicate devices.
• 7. The sample size for the training set: Not applicable.
• 8. How the ground truth for the training set was established: Not applicable.

In conclusion, the document describes a 510(k) submission for a physical medical device (bone plating system) and its validation through non-clinical mechanical testing (Finite Element Analysis) against predicate devices. The acceptance criteria revolve around substantial equivalence in design, materials, indications for use, and mechanical performance to legally marketed predicate devices. The validation methods are appropriate for a physical implantable device, but fundamentally different from those required for AI/software-based devices.

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The Emerge Medical Small Fragment Locked Plating System is intended for fixation of fractures, osteotomies, and non-unions of the clavicle, scapula, olecranon, humerus, radius, ulna, pelvis, distal tibia, and fibula, including osteopenic bone.

The System consists of stainless steel plates (including straight, reconstruction, periarticular t-plates, humerus, one-third tubular), standard cortex screws, locking cortex screws, standard cancellous screws, and washers. The plates are available in a variety of styles and lengths with the number of holes varying depending on plate length, and include threaded locking holes and non-threaded dynamic compression slots. The screws and plates are provided non-sterile. The Emerge Medical Small Fragment Locked Plating System is intended for fixation of fractures, osteotomies, and non-unions of the clavicle, scapula, olecranon, humerus, radius, ulna, pelvis, distal tibia, and fibula, including osteopenic bone.

The provided document is a 510(k) premarket notification for the "Emerge Medical Small Fragment Locked Plating System," which is a metallic bone fixation device. This type of device is an orthopedic implant, not an AI/ML medical device, and therefore the concepts of acceptance criteria related to algorithmic performance, study designs like MRMC studies, ground truth establishment, and training/test sets are not applicable in this context.

The "Performance Data" section explicitly states: "Mechanical testing performed according to ASTM F382 demonstrated that the device performs as well as or better than the predicate device. Clinical data were not needed to demonstrate substantial equivalence."

This indicates that the regulatory clearance for this device was based on mechanical equivalency to existing predicate devices, not on the performance of a diagnostic or assistive algorithm.

Therefore, I cannot provide the requested information about acceptance criteria for an AI/ML device, as it is not applicable to this submission.

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