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510(k) Data Aggregation
(90 days)
CLS STEM, WAGNER REVISION STEM, ALLOCLASSIC ZWEYMULLER STEM, SL
The CLS, Wagner Revision and AlloClassic Zweymuller femoral stems will be used in conjunction with the Zirconia Ceramic Total Head. This stem/head combination is intended for prosthetic replacement of the proximal portion of the femur during total hip arthroplasty in which the ball head articulates with a polyethylene acetabular component. Specific diagnostic indications include:
- Patient conditions of inflammatory degenerative joint disease (e.g., rheumatoid arthritis) and noninflammatory degenerative joint disease (e.g., osteoarthritis, avascular necrosis):
- . Those patients with failed previous surgery where pain, deformity, or dysfunction persist; and,
- Revision of previously failed hip arthroplasty. ●
The CLS Stem. Wagner Revision Stem and AlloClassic Zwevmuller Stem, are intended for use with or without bone cement.
The CLS Stem. Wagner Revision Stem and AlloClassic Zwevmuller Stem are all designed for use with ceramic heads. These stems are manufactured from Ti-6A1-7Nb alloy and have the same proximal taper which is machined per the specifications of the 12/14 Sulzer taper.
The Zirconia Ceramic Total Head is a spherical head with a 12/14 taper, making it compatible with the above stems. The heads are available in 28mm and 32mm diameters and three neck lengths; short, medium and long.
Static compression testing demonstrated that the burst strength of the zirconia heads exceeds FDA requirements.
The provided 510(k) summary (K973837) for the Zirconia Ceramic Total Head for use with specific femoral stems does not describe a study involving an algorithm or artificial intelligence (AI).
Instead, this document pertains to a medical device for total hip arthroplasty, specifically discussing the material compatibility and mechanical performance of a ceramic hip head. The relevant "acceptance criteria" discussed are related to the physical properties and performance of the device itself, not the performance of a diagnostic algorithm.
Here's an analysis based on the information provided, recognizing that the request template is typically for AI/diagnostic device studies:
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria | Reported Device Performance |
---|---|
FDA requirements for burst strength of ceramic heads | "Static compression testing demonstrated that the burst strength of the zirconia heads exceeds FDA requirements." |
2. Sample size used for the test set and the data provenance
- Sample Size: Not specified. The document only states "Static compression testing."
- Data Provenance: Not applicable. This refers to mechanical testing of a physical product, not a dataset.
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 testing would align with engineering standards and direct physical measurements, not expert consensus in a medical imaging context.
4. Adjudication method for the test set
- Not applicable. This is not a study requiring adjudication of interpretations.
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 device is not an AI diagnostic tool.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
- Not applicable. This device is not an algorithm.
7. The type of ground truth used
- Ground Truth Type: Engineering standards and direct physical measurements of burst strength during static compression testing.
8. The sample size for the training set
- Not applicable. There is no algorithm or training set mentioned.
9. How the ground truth for the training set was established
- Not applicable.
In summary, the provided 510(k) summary focuses on the substantial equivalence and mechanical performance (burst strength) of a physical medical device (zirconia ceramic hip head) and does not involve any studies related to diagnostic algorithms, AI, or human-in-the-loop performance for image interpretation.
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