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Hip components are indicated for individuals undergoing primary and revision surgery where other treatments or devices have failed in rehabilitating hips damaged as a result of trauma or noninflammatory degenerative joint disease (NID) or any of its composite diagnoses of osteoarthritis, avascular necrosis, traumatic arthritis, slipped capital epiphysis, fused hip, fracture of the pelvis, and diastrophic variant.

Hip components are also indicated for inflammatory degenerative joint disease including rheumatoid arthritis, atturitis secondary to a variety of diseases and anomalies, and congenital dysplasia; treatments of nonunion, femoral neck fracture, and trochanteric fractures of the proximal femur with head involvement that are unmanageable using other techniques; endoprosthesis, femoral osteotomy, or Girdlestone resection: fracture-dislocation of the hip; and correction of deformity.

The R3 Acetabular System is for single use only and is intended for cementless use.

The subject of this Traditional 510k is the R3 Large Head Liners 0 and 20-degree implants (28mm X 44mm – 36mm X 50mm). The R3 Large Head Liners are a line extension to the existing R3 Acetabular System. The R3 Large Head Liners were previously cleared for market via premarket notification K113848. The head size of the liners are being increased to include sizes 28mm X 44mm, 32mm X 46mm, and 36mm X 50mm (inner diameter and outer diameter, respectively) in 0 and 20-degree versions. The R3 Large Head Liners are intended for single use only.

The provided text describes a 510(k) premarket notification for the "R3 Large Head Liners" and focuses on demonstrating substantial equivalence to predicate devices, rather than the performance of a device based on a clinical study against specific acceptance criteria. This submission is for a medical device (hip joint components), not a diagnostic AI/ML device, and therefore the acceptance criteria and study types typically associated with AI/ML device clearance (like those around clinical performance metrics, reader studies, etc.) are not present in this document.

However, based on the information provided, I can extract the relevant "performance testing" conducted to support the substantial equivalence claim.

Here's an interpretation of the information within the provided context:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in the traditional sense of a numerical threshold for a diagnostic accuracy study. Instead, it refers to performance testing used to establish substantial equivalence to predicate devices. The "reported device performance" is that the device met the requirements of these tests.

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

• Sample Size: Not explicitly stated for each test. The tests are bench (mechanical) tests performed on the physical device, not on human data.
• Data Provenance: Not applicable in the context of human data. These are laboratory bench tests.

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

This is not applicable as the "ground truth" for these mechanical tests is determined by standardized test methodologies and measurements, not expert consensus on medical images or clinical outcomes.

4. Adjudication method for the test set:

Not applicable. Adjudication methods are used in studies involving human interpretation (e.g., of medical images) to resolve discrepancies.

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 a submission for a physical medical implant, not an AI/ML diagnostic device, so an MRMC study is not relevant here.

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

Not applicable. This is not an algorithm.

7. The type of ground truth used:

The "ground truth" for these tests are the objective measurements obtained from standardized mechanical and biocompatibility testing. For example, for "Lever-Out," the ground truth would be the measured force required for lever-out, compared against an established performance specification.

8. The sample size for the training set:

Not applicable. These are physical device tests, not machine learning model evaluations.

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

Not applicable. There is no "training set" in this context.

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