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The JPS JuniOrtho Plating System™ is internal fixation and stabilization of fractures, osteotomies, malunions and non-unions of long bones of the lower limb.

The JPS JuniOrtho Plating System™ is indicated for internal fixation of femoral and tibial fractures, osteotomies, mal-unions and non-unions.

Indications include:

• Varus, valgus, rotational and/or shortening osteotomies
• Femoral neck and/or pertrochanteric fractures
• Proximal and distal metaphyseal fractures
• Pathological and impeding pathological fractures

Use of the JPS JuniOrtho Plating System™ is indicated in pediatric (excluding newborns) and small statients.

The Subject Device is a line extension of the existing locking and non- locking screws with same indications and intended use, diameters and new lengths, and with a modified geometry, to be used in addition to those already cleared for use (JPS JuniOrtho Plating System K200246).
The predicate device, screws are offered both in sterile and non-sterile packaging configurations and are made from Stainless steel AISI 316LVM, according to ASTM F138 "Standard Specification for Wrought 18Chromium-14Nickel-2.5Molybdenum Stainless Steel Bar and Wire for Surgical Implants (UNS S31673)".

This is a medical device submission (510k) for a bone fixation system, not an AI/ML device. Therefore, the requested information about acceptance criteria and study details related to AI/ML device performance (like sample sizes for test/training sets, expert ground truth, adjudication methods, MRMC studies, or standalone performance) is not applicable to this document.

The document discusses the substantial equivalence of the JPS JuniOrtho Plating System™ (a line extension) to a predicate device, focusing on mechanical performance, biocompatibility, and intended use.

Here's the relevant information provided:

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

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not applicable as this is a medical device (bone fixation system) and not an AI/ML device. The "test set" refers to mechanical testing of the physical device components. Specific sample sizes for the mechanical tests are not detailed in this summary, but are typically defined by the ASTM standard or internal protocols.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Not applicable. Ground truth, in the context of AI/ML, refers to labels or diagnoses. For a physical medical device, "ground truth" relates to engineering specifications and performance standards which are met through physical testing and quality control processes.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not applicable. Adjudication methods are typically used in clinical studies or for establishing ground truth in AI/ML performance evaluations.

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 relevant to devices that assist human interpretation, particularly in diagnostic imaging or clinical decision support AI/ML tools.

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

• Not applicable. This pertains to AI/ML algorithm performance.

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

• Not applicable as an AI/ML ground truth. For this device, the "ground truth" for its performance is defined by adherence to established mechanical standards (e.g., ASTM F543 – 17) and engineering specifications, ensuring safety and efficacy consistent with its intended use and predicate device.

8. The sample size for the training set

• Not applicable. Training sets are used for AI/ML model development.

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

• Not applicable. This relates to AI/ML model development.

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