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510(k) Data Aggregation
(245 days)
The SixFix® Hexapod Fixator is intended to be used for post-traumatic joint contracture which has resulted in loss of range of motion; fractures and disease which generally may result in joint contractures or loss of range of motion and fractures requiring distraction; open and closed fracture fixation; pseudo-arthrosis of long bones; limb lengthening by epiphyseal or metaphyseal distraction of bony or soft tissue deformities; correction of bony or soft tissue defects; joint arthrodesis; infected fractures or nonunions.
The SixFix Hexapod Fixator is a multilateral circular external fixation system. The system includes the following external fixator elements: rings, arches, struts, threaded rods, reduction struts, and assembly accessories. All the elements are provided non-sterile and are for single use only.
This document is a 510(k) summary for "Accessories for the SixFix Hexapod Fixator". It is a premarket notification for a Class II medical device, and therefore a clinical study to prove the device meets acceptance criteria is not typically required. This 510(k) summary describes a performance data study, specifically a mechanical testing study, rather than a clinical study involving human patients or a complex algorithm.
Here's a breakdown of the information requested, based on the provided text:
1. A table of acceptance criteria and the reported device performance
Acceptance Criteria | Reported Device Performance |
---|---|
Comparable mechanical performance to predicate device | SixFix Reduction Struts exhibited results comparable to the predicate device in static axial compression testing. |
Slightly greater stiffness than predicate device (implied beneficial outcome) | SixFix Reduction Struts possessed a slightly greater stiffness than the predicate device. |
No new concerns related to safety and effectiveness | Confirmed by comparison of indications, material, design, and technological characteristics. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Sample Size: Not explicitly stated in terms of a number of devices or tests. The document refers to "The SixFix Reduction Struts" in the plural, implying multiple units were tested, but a specific quantity is not given.
- Data Provenance: Not explicitly stated. This was mechanical testing, likely conducted by the manufacturer or a contracted testing facility.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
- Not Applicable. This was a mechanical engineering study, not a clinical study requiring expert interpretation of medical data. The "ground truth" would be the measured physical properties of the tested components.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not Applicable. As a mechanical engineering study, there is no "adjudication method" in the clinical sense. The results are direct measurements.
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 a mechanical external fixator accessory, not an AI-assisted diagnostic or treatment device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not Applicable. This device is a mechanical external fixator accessory, not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- Mechanical properties/measurements: The ground truth for this mechanical performance study would be the measured forces, displacements, and stiffness values obtained during the ASTM F1541 static axial compression testing.
8. The sample size for the training set
- Not Applicable. This is not a machine learning device; therefore, there is no "training set."
9. How the ground truth for the training set was established
- Not Applicable. As there is no training set for a machine learning model, this question is irrelevant to this device submission.
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