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The OsteoCentric Integrated Hip Fastener System is intended to treat stable and unstable intertrochanteric, subtrochanteric, and basilar neck fractures in which a stable medial buttress can be reconstructed.

The OsteoCentric Integrated Hip Fastener System is intended for use in internal fixation of femoral neck fractures. The OsteoCentric Integrated Hip Fastem consist of plates and screws in a variety of lengths and diameters to accommodate different anatomic sizes of patients. The devices are provided non-sterile. All implantable devices are manufactured from Stainless Steel per ASTM F138.

The provided document is a 510(k) premarket notification for a medical device called the "OsteoCentric Integrated Hip Fastener System." This type of submission is for demonstrating substantial equivalence to a legally marketed predicate device, rather than proving safety and effectiveness through clinical trials with strict acceptance criteria and performance metrics.

Therefore, the document does not contain the kind of information typically requested for AI/ML device studies, such as:

• A table of acceptance criteria and reported device performance (in terms of sensitivity, specificity, AUC, etc.)
• Sample size used for the test set and data provenance (e.g., country of origin, retrospective/prospective)
• Number of experts and their qualifications used to establish ground truth
• Adjudication method
• Multi-reader multi-case (MRMC) comparative effectiveness study
• Standalone (algorithm-only) performance
• Type of ground truth used (pathology, outcomes data, etc.)
• Sample size for the training set
• How ground truth for the training set was established

Instead, this document describes an engineering analysis for a mechanical device.

Here's what the document does provide regarding the "study" that proves the device meets the "acceptance criteria" (using these terms loosely in the context of a mechanical device submission):

1. Acceptance Criteria and Reported Device Performance (Mechanical Context):

The acceptance criteria here are implicitly demonstrating substantial equivalence to predicate devices in terms of mechanical properties for the intended use.

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

• Test Set: No discrete "test set" in the context of an AI/ML algorithm is mentioned. The assessment was based on an "engineering analysis" comparing physical properties and design features of the subject device to predicate devices. This involves material properties, moment of inertia, screw types/sizes, and thread profiles.
• Data Provenance: Not applicable in the context of an engineering analysis of mechanical components. The data would come from specifications, material testing standards (e.g., ASTM F138), and design comparisons.

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 mechanical properties is established through engineering principles, material science, and testing standards, not expert consensus in the medical imaging sense. The submission was prepared by Nathan Wright MS, an Engineer & Regulatory Specialist at Empirical Testing Corp.

4. Adjudication method for the test set:

• Not applicable. There was no clinical or interpretative "test set" requiring 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 a mechanical bone fixation device, not an AI/ML diagnostic or assistive tool.

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

• Not applicable. This is a mechanical device.

7. The type of ground truth used:

• Engineering specifications and standards: The "ground truth" for this device's performance relies on established engineering principles, material science (e.g., Stainless Steel per ASTM F138 specifications), and comparative analysis against the known mechanical properties of legally marketed predicate devices. The aim is to show that the new device's mechanical integrity is equivalent or superior to existing devices for the same indications.

8. The sample size for the training set:

• Not applicable. There is no "training set" in the context of this type of mechanical device submission.

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

• Not applicable.

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The OsteoCentric Bone Plate and Screw is intended for fixation of fractures, and non-unions of the clavicle, scapula, olecranon, humerus, radius, ulna, pelvis, tibia, and fibula, including, but not limited to periarticular, and intraarticular fractures

The OsteoCentric Bone Plate and Screw System consists of implants and instruments designed for fixation to treat fractures, deformations, revisions and replantations of bones and bone fragments. The system features nineteen (19) types of plates and bone screws for fixation that are locking or non-locking, 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 F138), and offered in various widths and lengths.

The provided text is a 510(k) summary for the OsteoCentric Bone Plate and Screw System. It details the device's characteristics, indications for use, and a comparison to predicate devices, but it does not contain information about an AI/ML-based medical device or any study proving a device meets acceptance criteria related to AI/ML performance.

Therefore, I cannot extract the information required to answer your questions regarding acceptance criteria, study details, sample sizes, expert involvement, or MRMC studies, as these concepts are not discussed in this document. The document describes a traditional medical device (bone plates and screws) and relies on substantial equivalence to previously cleared predicate devices, primarily through engineering analysis and existing performance testing of those predicates (ASTM F543 and ASTM F382).

The 510(k) clearance process for this type of device does not involve the kind of performance studies (e.g., clinical trials, AI algorithm validation) that would generate the data you're asking about.

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