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510(k) Data Aggregation
(29 days)
The Zimmer Periarticular Locking Plate System is indicated for temporary internal fixation and stabilization of osteotomies and fractures, including comminuted fractures, supracondylar fractures, intra-articular and extra-articular condylar fractures, fractures in osteopenic bone, nonunions and malunions.
The Zimmer Periarticular Locking Plates and Screws are intended for internal fracture fixation. The low-profile periarticular locking plate is anatomically-contoured and has threaded holes which accept locking screws to create a stable, fixed-angle construct.
This document is a 510(k) premarket notification for the Zimmer® Periarticular Locking Plate System. It describes the device, its intended use, and a comparison to a predicate device. However, it does not contain specific acceptance criteria or details of a study proving the device meets acceptance criteria in the way that would typically be presented for an AI/algorithm-based medical device.
Key points from the document regarding performance:
- Performance Data: "Non-Clinical Performance and Conclusions: The results of non-clinical (laboratory) performance testing and analysis confirm that the proposed device is safe and effective."
This statement is very high-level and does not provide quantitative acceptance criteria or detailed study specifics. It simply states that non-clinical (laboratory) testing was performed and deemed sufficient.
Given the information provided in the input, I cannot fill out the requested table or answer most of the specific questions about acceptance criteria and studies as they would apply to an AI device. The document is for a physical medical device (bone plates and screws), not an AI/algorithm. Therefore, many of the questions (e.g., about ground truth, training set, multi-reader multi-case studies, effect size of AI assistance) are not applicable to this type of submission.
Here's how I would answer based on the provided text, highlighting what is missing or not applicable:
Acceptance Criteria and Device Performance (Based on provided text)
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria Category | Specific Criteria (if available) | Reported Device Performance (if available) | Notes |
|---|---|---|---|
| Safety | Not explicitly stated | Confirmed safe through non-clinical (laboratory) testing and analysis. | The document provides a high-level statement without specific metrics or thresholds. |
| Effectiveness | Not explicitly stated | Confirmed effective through non-clinical (laboratory) testing and analysis. | The document provides a high-level statement without specific metrics or thresholds. |
| Performance (Mechanical) | Not explicitly stated (e.g., strength, fatigue, fixation stability) | Non-clinical (laboratory) performance testing and analysis confirms safety and effectiveness. | For a physical orthopedic implant, mechanical testing (e.g., bending strength, fatigue life, pull-out strength of screws) would be critical. However, specific values or pass/fail criteria are not detailed in this summary. |
| Biocompatibility | Not explicitly stated | Implied by "manufactured from the same materials using similar processes" as predicate. | The materials (titanium or stainless steel) are standard for implants and generally considered biocompatible. Specific biocompatibility test results are not detailed. |
| Design Equivalence | "Same intended use, operates with the same fundamental scientific technology, is manufactured from the same materials using similar processes and is similar in design" as predicate devices. | The device is deemed substantially equivalent to predicate devices. | This is a regulatory acceptance criterion for 510(k) cleared devices. |
2. Sample sized used for the test set and the data provenance
- Sample Size: Not specified. The document only mentions "non-clinical (laboratory) performance testing and analysis." This implies a sample size of physical devices tested in a lab, but the number is not provided.
- Data Provenance: Not applicable in the context of clinical data for an AI/algorithm. This refers to laboratory testing of physical devices. No patient data (retrospective or prospective) is mentioned.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Not applicable. This is a physical medical device; there is no "ground truth" in the diagnostic or an AI/algorithm sense. Performance is based on engineering and material science testing.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not applicable. This is a physical medical device. Adjudication methods are typically used for establishing ground truth in clinical imaging or diagnostic studies.
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 physical medical device, not an AI-assisted diagnostic tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not applicable. This is a physical medical device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Not applicable in the context of an AI/algorithm. For this device, "ground truth" would relate to fundamental physical properties and performance under mechanical load, established by engineering standards and testing protocols (e.g., what constitutes failure under a given load).
8. The sample size for the training set
- Not applicable. This is a physical medical device. There is no concept of a "training set" in the machine learning sense.
9. How the ground truth for the training set was established
- Not applicable. This is a physical medical device.
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