(333 days)
External fixation components are intended for the treatment of bone conditions that can be corrected or improved by external skeletal traction or fixation, including osteotomy, arthrodesis, fracture and reconstructive surgery.
External skeletal fixator systems are comprised of various elements that, when used in conjunction with one another, form bridge constructs to which anchoring screws, wires and/or pins, may be attached. Bridge elements are designed to provide a framework for stabilization of bone fractures where soft tissue injury may preclude the use of other fracture treatments such as IM rodding, casting, or other means of internal fixation. External fixator elements consist of components such as straight and curved rods, tubes, rodto-rod, and rod-to-pin couplings and clamps, rings and ring segments, ring-torod, and ring-to-pin clamps.
Vanguard receives previously used external fixation devices from healthcare facilities. These devices are cleaned, inspected, tested, repackaged and returned to the healthcare facility.
It is important to note that the provided text is a 510(k) Summary of Safety & Effectiveness for reprocessed external fixation devices, not an AI/ML device. Therefore, the questions related to AI/ML specific criteria (like ground truth establishment for training, MRMC studies, effect size of human improvement with AI, standalone performance of an algorithm) are not directly applicable.
However, I can extract the relevant information regarding the general device, its acceptance criteria, and the study proving it meets those criteria based on the provided document.
Here's the analysis of the acceptance criteria and the study for the Vanguard Reprocessed External Fixation Devices:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria Category | Specific Criteria | Reported Device Performance (as stated or implied) |
|---|---|---|
| Material & Physical Properties | Not explicitly stated as numerical criteria, but implied equivalence to OEM. | Reprocessed devices are "essentially identical to the currently marketed OEM devices." No changes are made to the currently marketed device's specifications, and they possess the same technological characteristics. |
| Functional Performance | Not explicitly stated as numerical criteria, but implied equivalent function to OEM. | "Performance/functional testing demonstrates the devices are equivalent and continue to be safe and effective for their intended use." |
| Sterilization | Adequate sterilization to ensure safety. | "Sterilization validations demonstrate that the reprocessed devices perform as intended and are safe and effective." |
| Packaging | Appropriate packaging to maintain sterility and device integrity. | "Packaging validations demonstrate that the reprocessed devices perform as intended and are safe and effective." |
| Safety and Effectiveness | Devices are safe and effective for their intended use. | "Performance, sterilization and packaging validations demonstrate that the reprocessed devices perform as intended and are safe and effective." Concluded "substantially equivalent to the predicate devices." |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: The document does not specify the exact sample size for the "performance, sterilization and packaging validations." It generically refers to "reprocessed devices."
- Data Provenance: The devices are "previously used external fixation devices from healthcare facilities." This implies the data/devices for testing are retrospective (they were used previously) and likely originate from healthcare facilities within the United States (given the FDA submission).
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This question is not applicable as this is a medical device reprocessing submission, not an AI/ML diagnostic device. Ground truth, in the context of AI/ML, refers to a definitively correct diagnosis or measurement. For this device, "ground truth" would be the established performance and safety of the original (OEM) devices, and the study aims to show equivalence of the reprocessed devices. The document does not describe the use of experts to establish a "ground truth" in this AI/ML sense.
4. Adjudication Method for the Test Set
This question is not applicable. Adjudication methods (like 2+1, 3+1) are typically used in clinical trials or AI/ML evaluations to resolve disagreements among human readers or experts establishing ground truth or evaluating performance. This document describes engineering and validation testing of a reprocessed physical device, not an interpretative task requiring multi-reader 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
This question is not applicable. An MRMC study is relevant for diagnostic or interpretive AI systems where human readers' performance with and without AI assistance is compared. This submission pertains to physical medical devices, not an AI system.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
This question is not applicable. This refers to the standalone performance of an AI algorithm, which is not what this submission is about.
7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)
For this device, the "ground truth" is effectively the established safety and performance profile of the original equipment manufacturer (OEM) devices. The study aims to demonstrate that the reprocessed devices maintain that same safety and performance, making them "substantially equivalent." There isn't a "ground truth" established from pathology or outcomes data specifically for the reprocessed devices in this submission in the way it would be for a diagnostic tool.
8. The Sample Size for the Training Set
This question is not applicable. "Training set" refers to data used to train an AI/ML model. This document describes the re-processing and testing of physical medical devices, not the development of an AI algorithm.
9. How the Ground Truth for the Training Set was Established
This question is not applicable for the same reason as above. There is no AI/ML training set mentioned or implied in this submission.
§ 888.3030 Single/multiple component metallic bone fixation appliances and accessories.
(a)
Identification. Single/multiple component metallic bone fixation appliances and accessories are devices intended to be implanted consisting of one or more metallic components and their metallic fasteners. The devices contain a plate, a nail/plate combination, or a blade/plate combination that are made of alloys, such as cobalt-chromium-molybdenum, stainless steel, and titanium, that are intended to be held in position with fasteners, such as screws and nails, or bolts, nuts, and washers. These devices are used for fixation of fractures of the proximal or distal end of long bones, such as intracapsular, intertrochanteric, intercervical, supracondylar, or condylar fractures of the femur; for fusion of a joint; or for surgical procedures that involve cutting a bone. The devices may be implanted or attached through the skin so that a pulling force (traction) may be applied to the skeletal system.(b)
Classification. Class II.