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The EBI XFIX DFS® Rail System is a unilateral external fixation device intended for use in the treatment of bone conditions including limb lengthening, corrective osteotomies, arthrodesis, fracture fixation, acute or gradual multiplanar correction and other bone conditions amenable to treatment by use of the external fixation modality.

The system consists of fixation components and implantable bone screws. The EBI XFIX DFS Rail System is utilized in the following manner: bone screws are inserted through the patient skin and soft tissue and into the bone. The fixator frame of the EBI XFIX DFS Rail System is attached to the shanks of the bone screws.

The provided text is a 510(k) summary for a medical device called the "EBI XFIX DFS® Rail System," an external fixation device. This type of submission is for demonstrating substantial equivalence to a predicate device, not for proving that the new device meets specific performance acceptance criteria through a clinical study or a standalone algorithm study in the way modern AI/ML devices do.

Therefore, most of the requested information regarding acceptance criteria, performance metrics, sample sizes, ground truth establishment, expert involvement, and comparative effectiveness studies is not applicable to this 1999 510(k) submission for a physical medical device. These elements are typically associated with performance studies for software as a medical device (SaMD) or AI-powered diagnostics.

Here's a breakdown of what can be extracted and what is not applicable based on the provided document:

1. A table of acceptance criteria and the reported device performance

• Not Applicable. This document is a 510(k) summary demonstrating substantial equivalence to predicate devices, not a report of performance against specific acceptance criteria in a study for a new device type (like an AI/ML product). The FDA's determination of substantial equivalence means the device is as safe and effective as a legally marketed predicate device.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not Applicable. No test set or associated data provenance is described in this 510(k) summary. The submission relies on a comparison of technological characteristics to predicate devices.

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. There is no mention of a test set, ground truth, or expert involvement for performance evaluation described in this 510(k) summary.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not Applicable. No test set or adjudication method is described.

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 physical external fixation system, not an AI or diagnostic tool. Therefore, an MRMC study or assessment of human reader improvement with AI assistance is irrelevant and not mentioned.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Not Applicable. This is a physical medical device, not a standalone algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Not Applicable. Ground truth is not established or used as part of this 510(k) submission, which is based on demonstrating substantial equivalence to existing devices.

8. The sample size for the training set

• Not Applicable. There is no training set for an algorithm mentioned in this submission.

9. How the ground truth for the training set was established

• Not Applicable. As there is no training set, there is no ground truth establishment for it.

In summary, the provided document is a regulatory submission demonstrating the substantial equivalence of a physical medical device (EBI XFIX DFS® Rail System) to existing predicate devices based on its intended use, materials, and technological characteristics. It does not describe performance studies involving acceptance criteria, test sets, ground truth, or AI/ML evaluations.

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