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510(k) Data Aggregation
(200 days)
R3ACT Stabilization System
The R3ACT™ Stabilization System is intended as an adjunct in fracture repair and ligamentous injuries of small bones of the feet and ankles including the distal tibia, distal fibula, talus, and calcaneus, and as an adjunct in external and intramedullary fixation systems involving plates and rods. Specifically, the R3ACT™ Stabilization System is intended to provide fixation during the healing process following a syndesmotic trauma, such as fixation of syndesmosis (syndesmosis disruptions) in connection with Weber B and C ankle fractures.
The R3ACT™ Stabilization System is a fixation device comprised of a titanium alloy screw, UHMWPE suture, and a polyurethane component. It is provided in various sizes to accommodate patient anatomy. The device maintains fixation prior to weight bearing and then allows motion after weight bearing. The implants can be used in conjunction with the Baby Gorilla®/Gorilla® Plating System (K203511).
This document describes the R3ACT™ Stabilization System, a medical device for fracture repair and ligamentous injuries of the feet and ankles. It is a 510(k) premarket notification to the FDA. The information provided is for a conventional medical device, not an AI/ML powered medical device. Therefore, the questions related to AI/ML powered devices cannot be fully answered.
Here's the information extracted from the provided text regarding the device and its testing:
1. A table of acceptance criteria and the reported device performance
The provided text states: "All performance testing conducted for the R3ACTTM Stabilization System met the predetermined acceptance criteria or were otherwise considered acceptable." However, specific quantitative acceptance criteria and detailed performance results for each test are not provided in the document.
The document lists the following performance tests conducted:
Test Type | Reported Device Performance |
---|---|
Torque to failure | Met predetermined acceptance criteria |
Insertion and removal torque | Met predetermined acceptance criteria |
Static pullout | Met predetermined acceptance criteria |
Static bending | Met predetermined acceptance criteria |
Static axial dissociation | Met predetermined acceptance criteria |
Dynamic axial dissociation | Met predetermined acceptance criteria |
Bacterial endotoxin testing | Test results meet acceptance criteria |
2. Sample sizes used for the test set and the data provenance
The document does not specify the sample sizes (number of devices/components) used for each performance test. It also does not discuss data provenance in terms of country of origin or retrospective/prospective as these are typically relevant for clinical studies, which were not performed for this device.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
This question is not applicable. The device underwent performance testing (mechanical and material tests) on the device itself, not a clinical study involving human or image data with ground truth established by experts.
4. Adjudication method for the test set
This question is not applicable for the same reason as point 3. Performance tests on a physical device do not involve adjudication by experts.
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. This is a conventional medical device, not an AI/ML powered device. No MRMC study was performed, and thus no effect size related to AI assistance is provided.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
This question is not applicable. This is a conventional medical device, not an AI/ML powered device. No algorithm-only performance was evaluated.
7. The type of ground truth used
For the performance testing mentioned (e.g., torque to failure, static pullout), the "ground truth" would be the engineered specifications and expected physical behavior of the device components under various loads, as defined by engineering standards and design requirements. It's not "expert concensus, pathology, or outcomes data" in the context of clinical evaluation.
8. The sample size for the training set
This question is not applicable. This device is not an AI/ML powered device and therefore does not have a "training set" in the machine learning sense. The testing performed was for product performance validation.
9. How the ground truth for the training set was established
This question is not applicable, as there is no training set for this conventional medical device.
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